diff --git a/_doc/api/tools/index.rst b/_doc/api/tools/index.rst
index b591d00..05b36b2 100644
--- a/_doc/api/tools/index.rst
+++ b/_doc/api/tools/index.rst
@@ -1,13 +1,10 @@
-
 teachpyx.tools
 ==============
 
-
 .. toctree::
     :maxdepth: 1
     :caption: submodules
 
-
     display/index
 
 
@@ -15,12 +12,11 @@ teachpyx.tools
     :maxdepth: 1
     :caption: modules
 
-
     data_helper
     graphviz_helper
     helpers
     profiling
-
+    pandas
 
 .. automodule:: teachpyx.tools
     :members:
diff --git a/_doc/api/tools/pandas.rst b/_doc/api/tools/pandas.rst
new file mode 100644
index 0000000..58b06a0
--- /dev/null
+++ b/_doc/api/tools/pandas.rst
@@ -0,0 +1,7 @@
+
+teachpyx.tools.pandas
+=====================
+
+.. automodule:: teachpyx.tools.pandas
+    :members:
+    :no-undoc-members:
diff --git a/_doc/articles/2026/2026-03-15-route2026-ml.rst b/_doc/articles/2026/2026-03-15-route2026-ml.rst
new file mode 100644
index 0000000..b08082b
--- /dev/null
+++ b/_doc/articles/2026/2026-03-15-route2026-ml.rst
@@ -0,0 +1,60 @@
+.. _l-feuille-route-2026:
+
+=========================================
+2026-03-15 : feuille de route 2025 - mars
+=========================================
+
+site web : `sdpython.github.io <https://sdpython.github.io/>`_
+
+`Apprendre la programmation avec Python
+<https://sdpython.github.io/doc/teachpyx/dev/>`_
+
+Quelques jeux de données :
+
+* `Parcoursup 2025 - vœux de poursuite d'études et de réorientation dans l'enseignement supérieur et réponses des établissements
+  <https://www.data.gouv.fr/datasets/parcoursup-2025-voeux-de-poursuite-detudes-et-de-reorientation-dans-lenseignement-superieur-et-reponses-des-etablissements>`_
+* `Patrimoine immobilier des opérateurs de l’Enseignement supérieur
+  <https://www.data.gouv.fr/datasets/patrimoine-immobilier-des-operateurs-de-lenseignement-superieur>`_
+* `Prix des carburants en France - Flux quotidien
+  <https://www.data.gouv.fr/datasets/prix-des-carburants-en-france-flux-quotidien-1>`_
+* `Prix des carburants en France - Flux instantané - v2
+  <https://www.data.gouv.fr/datasets/prix-des-carburants-en-france-flux-instantane-v2-amelioree>`_
+* `Séries sur les surfaces, rendements, production céréales
+  <https://visionet.franceagrimer.fr/Pages/SeriesChronologiques.aspx?menuurl=SeriesChronologiques/productions%20vegetales/grandes%20cultures/surfaces,productions,rendements>`_
+* `Effectifs d'étudiants inscrits dans les établissements et les formations de l'enseignement supérieur - détail par établissements 
+  <https://www.data.gouv.fr/datasets/effectifs-detudiants-inscrits-dans-les-etablissements-et-les-formations-de-lenseignement-superieur-detail-par-etablissements>`_
+* `Résultats du contrôle sanitaire de l'eau distribuée commune par commune 
+  <https://www.data.gouv.fr/datasets/resultats-du-controle-sanitaire-de-leau-distribuee-commune-par-commune>`_
+* `Résultats du contrôle sanitaire de l'eau du robinet <https://www.data.gouv.fr/datasets/resultats-du-controle-sanitaire-de-leau-du-robinet>`_
+* `Données climatologiques de base - horaires <https://www.data.gouv.fr/datasets/donnees-climatologiques-de-base-horaires>`_
+* `Données climatologiques de base - mensuelles  <https://www.data.gouv.fr/datasets/donnees-climatologiques-de-base-mensuelles>`_
+
+Fonctions utiles:
+
+* :func:`teachpyx.tools.pandas.read_csv_cached`
+
+Séance 1 (6/2)
+==============
+
+Séance 2 (13/2)
+===============
+
+Séance 3 (27/2)
+===============
+
+Séance 4 (6/3)
+==============
+
+Séance 5 (13/3)
+===============
+
+Séance 6 (20/3)
+===============
+
+Evaluation
+==========
+
+* https://defis.data.gouv.fr/
+* le projet doit inclure au moins un graphe
+  *Partial Dependence* ou *Permutation Importance* (voir liens ci-dessus)
+* soutenance 11 avril 9h-13h
diff --git a/_doc/articles/index.rst b/_doc/articles/index.rst
index a12fb03..9b9602c 100644
--- a/_doc/articles/index.rst
+++ b/_doc/articles/index.rst
@@ -5,6 +5,12 @@ Collections d'articles périssables
 
 Ou *blog*.
 
+.. toctree::
+    :caption: 2026
+    :maxdepth: 1
+
+    2026/2026-03-15-route2026-ml
+
 .. toctree::
     :caption: 2025
     :maxdepth: 1
diff --git a/_doc/c_data/nb_dataframe.ipynb b/_doc/c_data/nb_dataframe.ipynb
index d7b55c0..cfa928d 100644
--- a/_doc/c_data/nb_dataframe.ipynb
+++ b/_doc/c_data/nb_dataframe.ipynb
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -524,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -962,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1039,7 +1039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1069,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1099,7 +1099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1126,13 +1126,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['date', 'prix', 'devise'], dtype='object')"
+       "Index(['date', 'prix', 'devise'], dtype='str')"
       ]
      },
      "execution_count": 23,
@@ -1146,13 +1146,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "221.0"
+       "np.float64(221.0)"
       ]
      },
      "execution_count": 24,
@@ -1247,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1276,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1311,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1397,7 +1397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1578,7 +1578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1657,7 +1657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1763,7 +1763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -1794,15 +1794,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['ville', 'annee', 'temps', 'secondes'], dtype='object')\n",
-      "villes {'BOSTON', 'FUKUOKA', 'CHICAGO', 'STOCKOLM', 'NEW YORK', 'LONDON', 'PARIS', 'BERLIN', 'AMSTERDAM'}\n",
+      "Index(['ville', 'annee', 'temps', 'secondes'], dtype='str')\n",
+      "villes {'AMSTERDAM', 'FUKUOKA', 'BERLIN', 'LONDON', 'STOCKOLM', 'NEW YORK', 'PARIS', 'BOSTON', 'CHICAGO'}\n",
       "annee [1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956] ...\n"
      ]
     }
@@ -1979,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2050,7 +2050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2071,7 +2071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2103,7 +2103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2210,13 +2210,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fd034df94b0>"
+       "<pandas.api.typing.DataFrameGroupBy object at 0x78ca1676a9c0>"
       ]
      },
      "execution_count": 41,
@@ -2231,7 +2231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2323,7 +2323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2386,7 +2386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2461,7 +2461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2539,7 +2539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2623,7 +2623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -2634,7 +2634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2888,7 +2888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2918,7 +2918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3353,7 +3353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3462,7 +3462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -3481,13 +3481,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.3328334999999999"
+       "0.3328335"
       ]
      },
      "execution_count": 57,
@@ -3582,7 +3582,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "784 µs ± 33.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "398 μs ± 10.1 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -3606,7 +3606,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "310 µs ± 8.6 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "139 μs ± 4.99 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
      ]
     }
    ],
@@ -3656,23 +3656,23 @@
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
        "      <th>1</th>\n",
-       "      <td>3</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>7</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>4</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3681,11 +3681,11 @@
       "text/plain": [
        "           autre\n",
        "cle1 cle2       \n",
-       "1    1         3\n",
-       "     1         1\n",
-       "     1         7\n",
-       "     1         4\n",
-       "     1         1"
+       "1    1         8\n",
+       "     1         5\n",
+       "     1        10\n",
+       "     1         6\n",
+       "     1         7"
       ]
      },
      "execution_count": 61,
@@ -3836,7 +3836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -3899,15 +3899,18 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'dbfread'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[64], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdbfread\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DBF\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdBase2df\u001b[39m(dbase_filename):\n\u001b[1;32m      6\u001b[0m     table \u001b[38;5;241m=\u001b[39m DBF(dbase_filename, load\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcp437\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dbfread'"
-     ]
+     "data": {
+      "text/plain": [
+       "((246123, 16),\n",
+       " Index(['ANAISH', 'DEPNAISH', 'INDNATH', 'ETAMATH', 'ANAISF', 'DEPNAISF',\n",
+       "        'INDNATF', 'ETAMATF', 'AMAR', 'MMAR', 'JSEMAINE', 'DEPMAR', 'DEPDOM',\n",
+       "        'TUDOM', 'TUCOM', 'NBENFCOM'],\n",
+       "       dtype='str'))"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -3935,14 +3938,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(16, 4) Index(['VARIABLE', 'LIBELLE', 'TYPE', 'LONGUEUR'], dtype='object')\n"
+      "(16, 4) Index(['VARIABLE', 'LIBELLE', 'TYPE', 'LONGUEUR'], dtype='str')\n"
      ]
     },
     {
@@ -4127,7 +4130,7 @@
        "15         1  "
       ]
      },
-     "execution_count": 281,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4149,7 +4152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4167,7 +4170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {
     "collapsed": true
    },
@@ -4188,12 +4191,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1800x600 with 1 Axes>"
       ]
@@ -4255,7 +4258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {
     "collapsed": true
    },
@@ -4266,7 +4269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {
     "collapsed": true
    },
@@ -4275,8 +4278,8 @@
     "import pandas\n",
     "\n",
     "writer = pandas.ExcelWriter(\"ton_example100.xlsx\")\n",
-    "df1000.to_excel(writer, \"Data 0\")\n",
-    "df1000.to_excel(writer, \"Data 1\")\n",
+    "df1000.to_excel(writer, sheet_name=\"Data 0\")\n",
+    "df1000.to_excel(writer, sheet_name=\"Data 1\")\n",
     "writer.close()"
    ]
   },
@@ -4328,7 +4331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -4533,7 +4536,7 @@
        "[5 rows x 32 columns]"
       ]
      },
-     "execution_count": 287,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4555,7 +4558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -4634,7 +4637,7 @@
        "4  0.732512   3925.907588         3       1.0"
       ]
      },
-     "execution_count": 288,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4646,7 +4649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -4732,7 +4735,7 @@
        "8401  0.343340   1840.132652         3       1.0"
       ]
      },
-     "execution_count": 289,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4745,7 +4748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4782,7 +4785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4792,7 +4795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4801,7 +4804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -4810,7 +4813,7 @@
        "1"
       ]
      },
-     "execution_count": 293,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4821,16 +4824,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((830, 5), (8403, 5))"
+       "((892, 5), (8403, 5))"
       ]
      },
-     "execution_count": 294,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4860,7 +4863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -4927,7 +4930,7 @@
        "4    FUKUOKA  8075.187500"
       ]
      },
-     "execution_count": 295,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4943,7 +4946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -5028,7 +5031,7 @@
        "4  PARIS   2007  02:07:17        7637  7937.028571"
       ]
      },
-     "execution_count": 296,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5041,7 +5044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -5171,7 +5174,7 @@
        "2011     8047.0  "
       ]
      },
-     "execution_count": 297,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5199,7 +5202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -5273,7 +5276,7 @@
        "moyenne     NEW YORK       PARIS     FUKUOKA    STOCKOLM  "
       ]
      },
-     "execution_count": 298,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5286,7 +5289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -5319,15 +5322,15 @@
        "      <th>NEW YORK</th>\n",
        "      <th>PARIS</th>\n",
        "      <th>STOCKOLM</th>\n",
-       "      <th>7695.1612903225805</th>\n",
-       "      <th>7815.909090909091</th>\n",
-       "      <th>7883.371428571429</th>\n",
-       "      <th>7891.061224489796</th>\n",
-       "      <th>7922.315789473684</th>\n",
-       "      <th>7928.5609756097565</th>\n",
-       "      <th>7937.028571428571</th>\n",
+       "      <th>7695.16129</th>\n",
+       "      <th>7815.909091</th>\n",
+       "      <th>7883.371429</th>\n",
+       "      <th>7891.061224</th>\n",
+       "      <th>7922.315789</th>\n",
+       "      <th>7928.560976</th>\n",
+       "      <th>7937.028571</th>\n",
        "      <th>8075.1875</th>\n",
-       "      <th>8133.393939393939</th>\n",
+       "      <th>8133.393939</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>annee</th>\n",
@@ -5470,32 +5473,24 @@
        "2011           NaN  7418.0  7382.0      NaN      NaN  7480.0       NaN   \n",
        "moyenne        NaN     NaN     NaN      NaN      NaN     NaN       NaN   \n",
        "\n",
-       "          PARIS  STOCKOLM 7695.1612903225805 7815.909090909091  \\\n",
-       "annee                                                            \n",
-       "2008     7600.0    8163.0                NaN               NaN   \n",
-       "2009     7547.0    8134.0                NaN               NaN   \n",
-       "2010     7601.0    7968.0                NaN               NaN   \n",
-       "2011     7589.0    8047.0                NaN               NaN   \n",
-       "moyenne     NaN       NaN             LONDON           CHICAGO   \n",
-       "\n",
-       "        7883.371428571429 7891.061224489796 7922.315789473684  \\\n",
-       "annee                                                           \n",
-       "2008                  NaN               NaN               NaN   \n",
-       "2009                  NaN               NaN               NaN   \n",
-       "2010                  NaN               NaN               NaN   \n",
-       "2011                  NaN               NaN               NaN   \n",
-       "moyenne         AMSTERDAM            BOSTON            BERLIN   \n",
+       "          PARIS  STOCKOLM 7695.16129 7815.909091 7883.371429 7891.061224  \\\n",
+       "annee                                                                      \n",
+       "2008     7600.0    8163.0        NaN         NaN         NaN         NaN   \n",
+       "2009     7547.0    8134.0        NaN         NaN         NaN         NaN   \n",
+       "2010     7601.0    7968.0        NaN         NaN         NaN         NaN   \n",
+       "2011     7589.0    8047.0        NaN         NaN         NaN         NaN   \n",
+       "moyenne     NaN       NaN     LONDON     CHICAGO   AMSTERDAM      BOSTON   \n",
        "\n",
-       "        7928.5609756097565 7937.028571428571 8075.1875 8133.393939393939  \n",
-       "annee                                                                     \n",
-       "2008                   NaN               NaN       NaN               NaN  \n",
-       "2009                   NaN               NaN       NaN               NaN  \n",
-       "2010                   NaN               NaN       NaN               NaN  \n",
-       "2011                   NaN               NaN       NaN               NaN  \n",
-       "moyenne           NEW YORK             PARIS   FUKUOKA          STOCKOLM  "
+       "        7922.315789 7928.560976 7937.028571 8075.1875 8133.393939  \n",
+       "annee                                                              \n",
+       "2008            NaN         NaN         NaN       NaN         NaN  \n",
+       "2009            NaN         NaN         NaN       NaN         NaN  \n",
+       "2010            NaN         NaN         NaN       NaN         NaN  \n",
+       "2011            NaN         NaN         NaN       NaN         NaN  \n",
+       "moyenne      BERLIN    NEW YORK       PARIS   FUKUOKA    STOCKOLM  "
       ]
      },
-     "execution_count": 299,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5514,7 +5509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -5644,7 +5639,7 @@
        "moyenne  7695.16129  7928.560976  7937.028571  8133.393939  "
       ]
      },
-     "execution_count": 300,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5670,7 +5665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -5680,7 +5675,7 @@
       "(246123, 17) Index(['ANAISH', 'DEPNAISH', 'INDNATH', 'ETAMATH', 'ANAISF', 'DEPNAISF',\n",
       "       'INDNATF', 'ETAMATF', 'AMAR', 'MMAR', 'JSEMAINE', 'DEPMAR', 'DEPDOM',\n",
       "       'TUDOM', 'TUCOM', 'NBENFCOM', 'nb'],\n",
-      "      dtype='object')\n"
+      "      dtype='str')\n"
      ]
     },
     {
@@ -5844,7 +5839,7 @@
        "4        6     26     99     9              N   1  "
       ]
      },
-     "execution_count": 301,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5857,7 +5852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -6033,7 +6028,7 @@
        "4        6     26     99     9              N   1    46    51  "
       ]
      },
-     "execution_count": 302,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6046,12 +6041,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -6066,12 +6061,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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TT1a1/9EkHmcxBpDuQhAEUftIABPR9bOlpcXzClNgLr/8cjz88MN47LHHMGPGDGd5R0cHstksent7Pe17enrQ0dHhtPFHJdmfS7VpaWlBQ0ND2cdECkwE5AyKQiIIgogDeQnkRDxu2GMdhSSlxOWXX44HHngAjz76KObMmeNZf+yxxyKRSGD16tXOso0bN2Lz5s3o6uoCAHR1deHFF1/E9u3bnTarVq1CS0sLDjnkEKeNKsNuY8soF5pCioB4BOQRBEEQQHzu2QwRTCFVkIp3yZIluO+++/Df//3faG5udnxWWltb0dDQgNbWVlx66aVYunQppkyZgpaWFnzxi19EV1cXjj/+eADA6aefjkMOOQSf/vSnsWLFCnR3d+Paa6/FkiVLHKvP5z//efz4xz/GVVddhUsuuQSPPvoofvOb3+B3v/tdRcdGFhiCIAiCIHDbbbehr68PH/7whzF9+nTn9etf/9ppc/PNN+Pss8/GokWLcPLJJ6OjowO//e1vnfWapuHhhx+Gpmno6urCpz71KfzjP/4jvvGNbzht5syZg9/97ndYtWoVjjzySHzve9/Dv/3bv2HhwoUV9ZfywKD6uH5DytiYJAmCICY6OmPQK0lRqzCWeWD2vqYLPF3dRIkYzuO95WtHtb/jBU0hRYDGGBijKCSCIIhaJ8kAPkLlZayJophjPWf5oCmkCJBSggwwBEEQtY8hJ2YUUj1CFpgIyAkZG6cwgiCIiYwBQAiJVAyy2UWZB6YeIQUmAkiXJwiCiA9xuWeTAhMOKTAEQRAEUYNwxsDJCaYoNe0DYxgGrrvuOsyZMwcNDQ3Yf//98c1vftMzfymlxLJlyzB9+nQ0NDRgwYIFeP3118e0nyP1ZicIgiDGngTds+uCmlZgbrrpJtx222348Y9/jFdffRU33XQTVqxYgR/96EdOmxUrVuCWW27B7bffjnXr1qGxsRELFy7E8PDwmPVTYwxJ+j4QBEHUPEkWnwK81RZyjCKKqZap6Smkp556Cueeey7OOussAMC+++6L//iP/8AzzzwDwLS+/OAHP8C1116Lc889FwDwi1/8Au3t7XjwwQdxwQUXjEk/pZQw4jKpShAEMYHJSyAhZSx8Q8gHJpyatsB86EMfwurVq/Haa68BAF544QU8+eSTOPPMMwEAmzZtQnd3NxYsWOBs09raivnz52Pt2rVF5WYyGfT393te1ZATEkZVEgiCIIixQADIUt6LuqCmLTBf/epX0d/fj3nz5kHTNBiGgRtuuAGLFy8GAKdOQ3t7u2e79vZ2Z10Qy5cvx/XXXx9ZP+mrQBAEER/ics9m1r9qZdQrNW2B+c1vfoN7770X9913H/785z/jnnvuwXe/+13cc889Vcm95ppr0NfX57y2bNkSUY8JgiAIIhrGuhp13KhpC8xXvvIVfPWrX3V8WQ4//HC8/fbbWL58OS666CJ0dHQAAHp6ejB9+nRnu56eHhx11FFF5aZSKacqZhQkOCOTJEEQRExIUhRSXVDTFpg9e/aAc28XNU2DEGbe2zlz5qCjowOrV6921vf392PdunXo6uoas35yikIiCIKIBUmG6nOrjBFkgQmnpi0w55xzDm644QbMmjULhx56KJ577jl8//vfxyWXXALAPLlXXHEFvvWtb2Hu3LmYM2cOrrvuOnR2duK8884bs35KKZEnAwxBEETNk5NAMjZRSFTMMYyaVmB+9KMf4brrrsM//dM/Yfv27ejs7MT//b//F8uWLXPaXHXVVRgcHMRll12G3t5enHjiiVi5ciXS6fSY9TMrZGycwgiCICYyEuY9Ow61kIhwmKSynOjv70drayv6+vrQ0tJS8fYZQ5ACQxAEESPS2sg8KKp9XlSyjznfPAU8XZ2dQQznsem6x0a1v+NFTVtgCIIgCGKiwhgHY9W5qla7fS1DCkwEJDhDjqaRCIIgYkFsopCicMKtYyeY+lXNxhDOWHy+EARBEBOYFI+gwjNRE5AFJgKElMhRHhiCIIiaJyMkkjweodSMMzBe5RRSHf+4JgUmAmj6iCAIIj5khUQ6BlFI5AMTTv0eGUEQBEEQdQtZYAiCIIgJRe3bXkyiyKQbh4R9I4UUmAhIWlFIYrw7QhAEQYTCEJ8oJIYIppDqeKKlfo9sDGGMQY/H94EgCGJCk2D1bZWYSJAFJgKElMiSFy9BEETNk7VqIcUiCommkEIhBSYCKISaIAgiPlAUUn1Qv0dGEAQxBkjrRRDE2EIWmAhgoBsYQUw0/N95+3Pt/64n4nKOaAopHFJgIiDBGfJCwhjvjhAEMa7U76OifuAw79lxgKaQwiEFJgIYY9CYhEFmGIIgiJpGj1EUEllgwqlf1WwMMSgKiSAI0FRyHMhK855NxB+ywERAnqKQCGLCEeT7Vr+/deuLnJDQKAop9pACQxAEMUJq/xFIxBrOzFe1MuqU+lXNxpD6vTwIgiDqD7pn1wdkgYmABGcwhER+vDtCEARBhKIB0GNilaAppHBIgYkAxhg4k+TBRxAEUeNosYpCqr6vMTnUEVG/qtkYQlFIBEEQ8SArKfCiXiALTATQl4EgCCI+5KWEHgNPGJpCCocUGIIg6hL1Z0Wpch9hj7KRbkcQ1UIKTDj1e2RjCA0iQdQWxeoUldu+2u2I2obu2fUBWWAiIKFxcCGQo7sZQcQSsqRMHHQGaDHxbKVSAuGQAhMR9XuJEARB1A8M8Xmo0xRSOKTAREBeSOTJ+kIQY4LftyVKuaqvTDwecUSl5CQghECC1/6DnSww4dT+GYwBVBiMIMaHYt+8Sm/Z0vfXfl9KTv0+Guobg27ZdQFZYAiCqClGWiAxbLson1ektBBjRgRTSKjjKaSaP7J9993XMaOpryVLlgAAhoeHsWTJEkydOhVNTU1YtGgRenp6xrSPMclKTRA1T6WKhv3VC9ouyKpSSg7zLaMf6vWJNt4dKJOgZ99IXvVKzSsw69evx7Zt25zXqlWrAAAf+9jHAABXXnklHnroIdx///1Ys2YNtm7divPPP39M+5jgHIn6vUYIYtwI+lox5VXptuW0D5NNX/P4k2Bm5CgRf2p+Cmnvvff2fL7xxhux//774+/+7u/Q19eHO++8E/fddx9OPfVUAMBdd92Fgw8+GE8//TSOP/748egyQRAEQVQNYxysSmfjeo5CitWRZbNZ/PKXv8Qll1wCxhg2bNiAXC6HBQsWOG3mzZuHWbNmYe3atWPWrxzlgCHqmHKnYmSJtpVM6QRtE/Yq1Z9y9lNO22LHVU5/iNogJ4GsIca7G2VBU0jh1LwFRuXBBx9Eb28vPvOZzwAAuru7kUwm0dbW5mnX3t6O7u7uonIymQwymYzzub+/v6p+USkkot5RFYlSt0O/ohLkTFtMTpDfSbVfrzA/maB2pfpT7v6I2iUe6gtRilhZYO68806ceeaZ6OzsrErO8uXL0dra6rxmzpwZUQ8JovaI2kJQqZxyLTejZb2IWi5ZW4ixwk5kV+2rXonNkb399tv44x//iM9+9rPOso6ODmSzWfT29nra9vT0oKOjo6isa665Bn19fc5ry5YtVfVNr2MTHRFvxvIBW0lBxHKtItXsMwo5ZH2pT+Jyz6YppHBio8DcddddmDZtGs466yxn2bHHHotEIoHVq1c7yzZu3IjNmzejq6urqKxUKoWWlhbPqxo0zpCs32uEmOBUcmmHtS03eqicfahy6KtHVEKSATrlvqgLYuEDI4TAXXfdhYsuugi67na5tbUVl156KZYuXYopU6agpaUFX/ziF9HV1TWmEUhSSppTJQiCiAFCAkzKWFgmTAtKtVFItX+cIyUWCswf//hHbN68GZdccknBuptvvhmccyxatAiZTAYLFy7ET37ykzHtX15IGGO6R4Ioj2qSsalOuFEmdYuq1lCUNYuiqoNUjpMzMb7kAQghkdRq/0xRLaRwYqHAnH766ZBF6g2l02nceuutuPXWW8e4Vy5kfSFqGb/yUW4Ukfo5SIkplezN3zboG1xKMQraZzE5/n1Wsh//tqoiU87+/f0gapu43LOpGnU4sVBgCIKojmqdVMPyuozm9pWEVI90XaX7D6uxNNI6TgRBVA4pMBGgc4YcJYMh6oio6wBFFXVUjCjklnvM/ummctoStUUiLtMqjFdfjJEsMEQYGmNgTCJLOgwRc0b7tj5aitFYyYkqIosYP5IM4DGJQiIfmHDqVzUbQ6SUMEh5ISYaFVzzkXw9/Jnj6DtHjABDoqhPJREvyAITATlBYdREbVKJ867avqjPjAQYM/+WK7MsOQGC1GcMY4V9k9Z/UfzALHeqp5IoJZo+qk0MmFFIqVhEIZETbxikwEQA6fJELTISp1Z7iid022LhOhX2J0yO/wey+tnzvvRuHbGVRCGFyfG/D4tYImqTuNyzOWPgVWro1W5fy5ACQxAEgPIeuEEROCNx0C0mJ8i0z6zWQfurtM/VUCpsPMp9EQRRmvq1LY0hiZg4hBETi7C0/aXS8bOAds4y6w33bRy0v0rl+K0/turCmPle/apxprYp3FfFxxXQ1t+GqA/ics9mEf2rhCeeeALnnHMOOjs7wRjDgw8+6Fn/mc98pqDW0hlnnOFps3PnTixevBgtLS1oa2vDpZdeioGBAU+bv/zlLzjppJOQTqcxc+ZMrFixouLxIQUmAjijWkhE7VLqARz2kC9oy4L/Bu2vWjnmcjcKQ21bsH3Avio6riJ9J+qPJDMjR+PAeFSjHhwcxJFHHhmaHPaMM87Atm3bnNd//Md/eNYvXrwYL7/8MlatWoWHH34YTzzxBC677DJnfX9/P04//XTMnj0bGzZswHe+8x18/etfxx133FFRX2kKKQKklMjHZVKVIBRKTcOErS85hWM1CHTUVb14Sz1MrDZ+OdX0TV1PzrYTi7wEErGqhVRdPyvd/swzz8SZZ54Z2iaVSqGjoyNw3auvvoqVK1di/fr1OO644wAAP/rRj/CRj3wE3/3ud9HZ2Yl7770X2WwWP//5z5FMJnHooYfi+eefx/e//32PolMKssBEAEUhEXFDnaqRvmX+5WHrPG2k+1fIItl3pTRfqlxrGVCoSJhtmW9Z6b77+xbU53LGgKg/BIDsBEw82t/f73llMpkRy3r88ccxbdo0HHTQQfjCF76AHTt2OOvWrl2LtrY2R3kBgAULFoBzjnXr1jltTj75ZCSTSafNwoULsXHjRuzatavsfpACEwET76tAjCZRPUArSZsSlhLfv06996vKh71OVQbsz+Z76VEQgj4z5p2xt389qnLC+hrU96jGoJicSvZB1AZxOUcMPJIXAMycOROtra3Oa/ny5SPq0xlnnIFf/OIXWL16NW666SasWbMGZ555JgzDLGnc3d2NadOmebbRdR1TpkxBd3e306a9vd3Txv5stykHmkIiiBoh6AE6EuNxJcpIRXJ9iotHpmNVKd7jcqKVhGKdcfdTval/pMddyViGRIUTxIiIcgppy5YtaGlpcZanUqkRybvggguc94cffjiOOOII7L///nj88cdx2mmnVdXXSiELTATExaOdiB9j/UuxmKNrqcSltpphRws58lhl0TzBSoH0uMn47+ejHSZdjby4/NKfaCQn4D27paXF8xqpAuNnv/32w1577YU33ngDANDR0YHt27d72uTzeezcudPxm+no6EBPT4+njf25mG9NEKTARABnDKkJ+IUgRpfxioYZ8T6VkCA1Usj+7F1Yrkh3m6DNa1V5GW25xMhJsvgkd2OMgTNe1Wu0nZXfeecd7NixA9OnTwcAdHV1obe3Fxs2bHDaPProoxBCYP78+U6bJ554ArlczmmzatUqHHTQQZg8eXLZ+yYFJgKklBPSKYwYXcbrinKsLRU64xQkoVO8eqXq4VtRX6SzjfLW6VdUJW3o2ztxyElzqjIOjEcemIGBATz//PN4/vnnAQCbNm3C888/j82bN2NgYABf+cpX8PTTT+Nvf/sbVq9ejXPPPRcHHHAAFi5cCAA4+OCDccYZZ+Bzn/scnnnmGfzpT3/C5ZdfjgsuuACdnZ0AgE9+8pNIJpO49NJL8fLLL+PXv/41fvjDH2Lp0qUV9ZUUmAjICkk3QGLEhOkJ5TidSt+r2r4Aiu6B8pWEsJBmJ9rIvx+E95kFSLajkGr9GVTj3ZuwSJiRo0Qwzz77LI4++mgcffTRAIClS5fi6KOPxrJly6BpGv7yl7/gH/7hH3DggQfi0ksvxbHHHov//d//9UxJ3XvvvZg3bx5OO+00fOQjH8GJJ57oyfHS2tqKP/zhD9i0aROOPfZYfPnLX8ayZcsqCqEGyImXIGoW24cizI21nNuw6ovhd6QN8tMoJdNWKhhzf8naJnknHJoxCOWzLdeTCiZAtuZEHblyPJaXMvsadpzFnImjGMugz0TtERf1ZTyKOX74wx8Ordb9yCOPlJQxZcoU3HfffaFtjjjiCPzv//5vRX3zQwoMQdQgfsWi2EOxlKNoMTlBcks9eFVHXTN82l1XYJKXZvYWzpirjChyOGzlRHoUG9VKY38GGDgrVGKc/viOq5RSFqb0lLYGFc/wW+wzUXvE5RyNRyK7OEFTSBGQ5JXOMhKEi99Zt9zolaD09yOR4++HraQ4y61IIr+feujDXsnn4spkjlKjwhmzHHRZEZnepUH9KTfKySMHKFBKwsaSqA8YKHK0XiALTAQwxpDkQIbmVYkqGK+ImoL2djBRwBO8oh9zTvRQYQw0K1gXLFjdtur+FOvmCNcR8SQRpyikETjhBsmoV0iBiQBBUUhEBJSawvEvD/IpKZgKUhcWcUCRcKeF7Gkd+y9gKRHW9k4Jo1LH4vFhcaeQpLIs6G+5cgBW0J9qbtPFhqmcbYh4kZVAUspYKDHj4QMTJ0iBiQDyaCeqoVxn0qDlob4evlDjYg60DK4/i7B8V2xlQToCTGdatW3QA1yNLuIo9GkBAMPah529V0jptA3y2Snoj3J8avtKv4XFxq4cXyD/+9p/FBIqWSGR1mr/rNnFAKqVUa+QAkMQMaHg4a58CPoxWfBAtxUZFvwQLvZZWEsZGAzfPm0lgnMGoaQTYAyFbX37UD8L2H4tzFFsvPu35CBYwSlFpf5AftmkqBBE7UEKTASM5OZIENVYDIJyxtnOrcUMglJpW4h7FdsOt/66RGqkkfTJEb6d+vdhKydSFq/cLqFYZ3x99siyQqudvhZp55ddCr+cILlBUVtE/IiLIsoQwRRSHcfqkAITAQnOkBPFb8wEMdpUF1Thbqz6BYQpTH7CLCGcKVE+ttmmDPytPD68o6RBlGvNsf+SH0z84IhPFBKFUYdTv6rZGMIYg16/1wgxYWAhn2oQFk0UkiVqRO1qfoyIAnRW3w/1iQRZYCJASIks2ZOJccSZUqnyvmzKKXSYrVYmj+AXr0Rh+GtUFpBy5ZAvTPzJSiAhpZP1uZZhlhtvtTLqFVJgIoCikIiREPUsiFIMGhgF2Uy6zrfc2o9hrbdvkbYiwOA636oh2nZbCa/SEKQYBK6znX18baody4rS21SxH6I2yAkJLQZRSDSFFA4pMAQxChRzBg2agij24A10KrUW+FPr+3PF2dFB7meprPfmePGvE9K7XzvtP5gbFm07+jJfKQBVDvPJ9bxXcrr4+6BK8/c9SHlRP1c0lkXk+LfzE9xTgiDGGlJgIoAiEgiV0Nws1t9KI2ekLPzsb2w/2/1tg+oUqe+Zb50aMQS4yoeQ0olwkj45piJSvINcscAIKT0h1lrgiJkjVBDNpFhgRjqW/s+VTAsV6ykRL+JyziiRXTg1f2TvvvsuPvWpT2Hq1KloaGjA4YcfjmeffdZZL6XEsmXLMH36dDQ0NGDBggV4/fXXx7SPCc6gjekeiVql3HDdqFGVB+/DuTLVupjJWirKS1GKrPdL8+SHqah34ZFJ1TAS5YWIJxrM+nVxwJyOrfZf/VLTCsyuXbtwwgknIJFI4Pe//z1eeeUVfO9738PkyZOdNitWrMAtt9yC22+/HevWrUNjYyMWLlyI4eHhMesnYwwxmE4liIqoeP49su9AcUH0NSOqRaMopLqhpqeQbrrpJsycORN33XWXs2zOnDnOeyklfvCDH+Daa6/FueeeCwD4xS9+gfb2djz44IO44IILxqSfhpTI0c+zCcdIpw/82zn+KorXqsfPooydqNFDDHZV58ouSjM5XaHfipTh5QOC+2LLcGXJgnkvNyFe4TEWM+eMbxQSEX+yEkgICS0GVhjOWNU1m+JQ82mk1LQF5n/+539w3HHH4WMf+ximTZuGo48+Gj/72c+c9Zs2bUJ3dzcWLFjgLGttbcX8+fOxdu3aonIzmQz6+/s9r2rIUxTShEX6XuXeKoJ8KaQs9F+xKXUPKpw2cp1kPXIClrmtzcgh+3NeSOTt8gBMyeRbpH+qk7Hfx8RQahmpfQjyXwk71GJKULWUc97q9zEw8ciVmUxxvGFONaTqXvVKRUf21ltvBd5ARou33noLt912G+bOnYtHHnkEX/jCF/DP//zPuOeeewAA3d3dAID29nbPdu3t7c66IJYvX47W1lbnNXPmzNE7CGLCMVoPOlagLnkJXupVFhzlQboKk9oGMEsR2JFIUvlso940uPKCDOiD9L6VsJQdZpvyfRFUVsvCY7G8AUb4a7LYVkEKnR+/gup/EcRoYU/jVvuqVypSYObOnYv33nvP+fyJT3wCPT09kXfKRgiBY445Bt/+9rdx9NFH47LLLsPnPvc53H777VXJveaaa9DX1+e8tmzZUpW8+r08iDD8DzDbmlBMifC3teGsdCmAoKrODNJJ029HDnHGCj6rlhNDAnnpWloASwFhzFFEdM7MFzOzlvo/a4rTurBeeenmhAFM5UTnDBp35XLGoDHvZ/+42PKc40ahQhQ2lsWUijDFLoxAS1kZ2xG1Tf3aJCYWFZ1Hv/Xl//2//4fBwcFIO6Qyffp0HHLIIZ5lBx98MDZv3gwA6OjoAIACJaqnp8dZF0QqlUJLS4vnVQ0JTqUEJjKV/BL3T5+E5iDxWScK5FgrC6wYyudSFlPVobHUrzV1vQzpuK08efpTRE6pcVPjKMKmw8bKGkJWl/ijIz61kGCFUVfzAoVRjw8nnHACNm7c6Fn22muvYfbs2QBMh96Ojg6sXr3aWd/f349169ahq6trzPrJWD3PMhLxI5rHbHTm5xBNjCDGGB6jKKTqQ6jrO5C6oiikoBvaaF4IV155JT70oQ/h29/+Nj7+8Y/jmWeewR133IE77rjD2fcVV1yBb33rW5g7dy7mzJmD6667Dp2dnTjvvPNGrV9+DEFRSPWKJxooQjlS+Wsv8zu0FkQnBcm1EsQVy7RbCgEzL0ZQ+yA59rJQR1ufvDA5pZCw98UKxgwoPpblMBI5FK0Uf7IS0IWEHhcrDFGUihQYKSU+85nPIJVKAQCGh4fx+c9/Ho2NjZ52v/3tbyPp3Ac+8AE88MADuOaaa/CNb3wDc+bMwQ9+8AMsXrzYaXPVVVdhcHAQl112GXp7e3HiiSdi5cqVSKfTkfShHPIx8WgnqqOch1exqBp/VA4CljGroWc/1pugB779WfjS+duZd+0SAFy6TrEaMxUj28+Ew/WvsZdzqy+GFYFk3+cNaWbU1bntuyIdZ1+NuQ6/zvSVtQ/D8gC25QgJMKmEsSr+PbYlUyjHwxTlhbHSY2kPW9i3suxzUkIOEU/yUkKPgSpKmXjDYbKCsKKLL764rHZq3pY40N/fj9bWVvT19Y3IHyZjCLrJ1SGVOH6O9Px7cqaEyFajc+y2fodSprS3rTdOeLO1zFEirLa2ApNz2jNnO39NJCjb2M7DqhzAUnIUBSbo+BxlhRUu81tbPBtGSLmKSe0/4oiRktZG9mCv9nlRyT4++fCPkGxsqEpWdnAI9539xVHt73hRkQUmborJWMGZNz06EX8qOZ0jPvUy9KMHv4rsb2tbW+ykc45y42vov05V66GEGWWk1kEyUIgACsoKqO2EdK04gO3Ua71nzFObSY04svvAlP/t7aOm2FirU0lE/VK/NomJRVXn8Y033sAjjzyCoaEhAKUjHuqVBOdI0B2v7onqFI9GJIsaSTTSb6EaPVSNHHN796+ajbf8nnjlhLUc6ViygJd370Q9kmBAcoTWl7GGEtmFM6Ij27FjB0477TQceOCB+MhHPoJt27YBAC699FJ8+ctfjrSDBEEQBDERoUR24YxIgbnyyiuRSCSwefNmTJo0yVn+iU98AitXroysc3EhJwRFIdURYadSdfasJKmZk/VW2p9dSUH/7DVBPVIz6EqfPDVCyJxOkkUto842yl5EgJyRoO7Ske/rS/GxU9oox6m+gltX2Ed4z6H/3BL1SU4COcM/eUnEkREVc/zDH/6ARx55BDNmzPAsnzt3Lt5+++1IOhYnqBRS/VCu8lKNfFs5KMytq7ZzlRjmGIGl51pTH+6G9Yaxwky2diONMcepNxeQ2RcAckKCM3Of5dzi7V9AdnRT3vosADDFkViNKgobP1Oed/oqbGY6qq9eWBQSUX8YABLj3YkyiCKPC+WB8TE4OOixvNjs3LnTCbEmiIlIsYd02MM7OBLHfYC66SrMgGIGV2nmluIQ9LDVGIOEhGEpEmr1XQ2mspC3lCmdMQhFQVLz0mjWG1sJ4UqEkubxb5HQ4a6zHXlLOcyq+3PM3b4IqJHkeVG3BaJTdghirKAw6nBGdGQnnXQSfvGLXzifGWMQQmDFihU45ZRTIutcXNDqeI5xohHmyFmJk2extqqjrFoLiMEtiuj5zNRHt7vOrjVkKyYas2oqAU7NIs7MazPJGRIa90zfcEtGijOkNA7OTGf0JGfQGQO3ZCSYGzatK/vUlfIZnlIEyjo7TDrouDSrrV0byaO8+I7V1rvUsOtS4xxEpeeWvtX1ixaTk0s+MOGMyAKzYsUKnHbaaXj22WeRzWZx1VVX4eWXX8bOnTvxpz/9Keo+1jw6Z+BCIks/8eqCUVFifBuoD/3yJQU85CuQo7aJQk5Q2+jkBH8OEjuaSgxRfyQYoPH6tUpMJEZ0Fg877DC89tprOPHEE3HuuedicHAQ559/Pp577jnsv//+UfcxFpDuQlRNkIdq0aYh/jMhjrtRyqksbQJ9Q4jawPZDiwNmEHS1/+pXJR+RBQYAWltb8a//+q9R9iW25AwRmPCLGD9sn5BiPifFfFRQZF3VcqTlDxKQXa6gTIBS30j9a65SI3SkZ3u/nGJ1kvzbj0ROEKXkAEHLbCtMoaxCOaoFKbpzS0ws8tKMtkvGYB4piikgmkLy8Ze//CVwOWMM6XQas2bNmlDOvBSQV5sUe9yWE2kUtRzzV19wWyG84cwMcOoTMektOmc7yNovtZYRE8KsYWQJ0hgghXTqGyWU5F15IaywabetEHZNJWk66lqPdw6z83b/ONxrXrOciu2bpJDevqvTZ+pxmZ+VG6vtPOz68PoUrPJuwpWeW2JiQvfs+mBECsxRRx1V8ItQ1fISiQQ+8YlP4Kc//emYFlUk6pfR/lVd6cOtmDLiVUWCZdvOqf48JHZb/83VDG0GmHTX2YpAHu7xcjtM2lpnKzJ2mLXIC2icgUMir+yUwVtiwJXj9kcdU7V/doQTtwtLBoyH6lsSNDLqGHCJYEcXhUrPLykuRFzhjDlO/9XIqFdG5APzwAMPYO7cubjjjjvwwgsv4IUXXsAdd9yBgw46CPfddx/uvPNOPProo7j22muj7m9NQmXZR5eR/KoejTNiywyfSQlXXtzIIq9cVaHxb28rI556Q0pbCSBpRyL55Kt1kQRMZSgb8vPTK8ftWxgSloIEr7ICwMk7w5Xlqsyg41W3h/LZluVfXin0bSX0mDzUoykkEI9jHQkjssDccMMN+OEPf4iFCxc6yw4//HDMmDED1113HZ555hk0Njbiy1/+Mr773e9G1tlaxUwQRlFItUYx35VqZSICucxyiJGK0FJ+hcUmUuxCjk47EW4gLyZHnQoy5YzsKG1lo2B5gB9PcSFF0m8VUWJKSYxC8SHqgyQDOP3orAtGpMC8+OKLmD17dsHy2bNn48UXXwRgTjPZNZLqHSklZeOtIcr3mBgfVKfcUZE/apLHCClLTiMRxEgxpOWfFYNrjJx4wxnRFNK8efNw4403IpvNOstyuRxuvPFGzJs3DwDw7rvvor29PZpe1jg5IZ0U6kRlqNMgtR6U69YOqrS3Lp5SAM5/JqW+jKzIe0MCeUUwV0wSjBXKVe9nfl8iQ7ih01VlylCGaLTOayVnYeRnjKg3DADZmPziZJYPTDWvelZgRmSBufXWW/EP//APmDFjBo444ggAplXGMAw8/PDDAIC33noL//RP/xRdT2uYeHwVahe/E2sxc39h8G3xdWG+MSNZZ+/DHxZsbudVLczZIXudiSFd2UKaTq+qvweDlTkXasp+M3zYjiJy92jO4UspMWzNFmWFRE5IaEoUUpK5N0ApzZICduSoPclkRzHZxUgNq39MyoIxtcPAHd8Yxpzij+p5sNcZylgJwAwP98tEQBi5mnAPpc9tOd+/+r2FEyOB7tn1wYgUmA996EPYtGkT7r33Xrz22msAgI997GP45Cc/iebmZgDApz/96eh6SUx4Sj20Sj2gyvFdqeRB6FW0vDYZJpQwZ+thrzE4Ycu2smJ/Vi0dAm5kD6RwFAoz9b4bPZTg5i+rtOZaYBLcLAEAKxzaduaV0lRuDAlIXxSSzk0FJ8ncfdt9VkOs7b5x5pXr1E5iXkdkLqVH6ZLwKmGqv43paKycQb+VShl71WeoGMWUHFJiiLhhu+FWK6NeGXEiu+bmZpx88snYd999namkxx57DADwD//wD9H0LibonCEXE5NkLVJOlEsp/A+okf5SVwlSVpzkaspn1dJgR/2onzl3t9G5kphOSk8xxLyVswUwE22pUUd5n+OrowTAqlGkJOVKWvlebKvGkBIjbYdkB2FHHtnY0XVBU0pCBlTGLibXn3JBPQ5Z/nmxFUL7PfP9LWd7UmIIwPwBEAcojDqcESkwb731Fj760Y/ixRdfLMgUCgCGMbHy0lIU0siJ4qtVTEa1slUlAXAtBpXMKdvTSUF1iPxyKrl8/JFHwfsOzp6r+sj6I4+KyamUsDpJI4XB7bt/CrFcCxxBJFl8HuqkwIQzItvSl770JcyZMwfbt2/HpEmT8NJLL2HNmjU47rjj8Pjjj0fcxdrH9i8gYkaYZ6dvnfTUKRr5yVbrC1Vbj6WYnEpqIYX1I0iO8zlokyK7rLQ/5UJfOWIk5GX13z2iNhiRBWbt2rV49NFHsddee4FzDk3TcOKJJ2L58uX453/+Zzz33HNR97OmySmmf6J6gpx1y2nPiryHbXFQpivUWjqefVhmF3W6wY0+cq0AjgOvla9ElavuRyg+JU6KfWV3Qrqp+Jn9Wf3FpGyg7idr+bzYLfOWAy9jTEnzb/1VHHtV/xvAvHZtOWr/HP8UFO97MaQEDGllD/ZbmXy+upU8RjznxLes1DVT7BohJh4C5vcnFYdaSIggjLqOr/QRKTCGYTjOunvttRe2bt2Kgw46CLNnz8bGjRsj7WAcIF0+OoLG0v/QKubzIEPe+39wFQQTqU6oUt2uiAXC3odaKNF+cWaGI1vLhfVA9+xOifIRQoJBUYI9xRfN+Xp7ymjYEI7vSd6Q4ExxpAXAFTkcruLFAcfJlzGGrCGcdrZfjHqkXOmD8EUkcUhnvKy34Jx5/GIkLKVJSjeoSErPzTToRzBnXr8YO/LJHZDSvk3FriG/wY18YiYucblnR5FJlzLx+jjssMPwwgsvYM6cOZg/fz5WrFiBZDKJO+64A/vtt1/UfSRiSrGbRKVfp3IeUCNBKg94+0EcJlv1hxGyeN0fLoUnbNrZB9xU/xozt1cdd+2QaTtSyf6BaBgSGkxfKzvfi9MP6x0HkNRMRSdrSCe1vw2H6QjMJcCYsk+4FhY7+63GvFFItnJjW3EC/dUtraNA2VR9bXzrVNRx9Yutdgq/fm/fBDGxGZECc+2112JwcBAA8I1vfANnn302TjrpJEydOhW//vWvI+1gHEhwFpvESLWOJ0IlIhlOheMSAsNWO7V8GIPwTRmq2wlZfB1gRvY4UzRCeHynVKXHthYwS8PIy8JIJMBVDBp07lqHfJoYh6uoGChcZx8XYEY02Z+52rDU2KlWLOk9B+a0letQbFtV1L9h4USllJgwxdO/mborYuKSjEkUEmXiDWdECoxaA+mAAw7AX//6V+zcuROTJ0+u68EqhplHg6KQyqGSq6PWriSmmhNGeK69/iXhgkp9ldxoJl//fDLLlgPvzc77XZYlj5kVvFG2Vnx7VKXS2UWRPpbsewXrSuyKmCDEKwqJg7Pq8rhUu30tE9mRTZkyZUIqL4B5c86T8lIzjNapiCpyISgfSnC7cgWGb1Bxt5XopsqimSrcT9mCx3Qzos7Jyei+y8T4Ur+q2RiSpSiksonrbcPnR+pJ+Kb6d5ip+31tlW1VZ17OvAm1ktydwgFcXxRYMrzrXHdYCbOGkW2uSHAGNcAirD/+gCfVYVfC9KOxrSf+oI0CC0eAv4stZ8SUmO+J6/VEjB8S8amFZDvxVvuqV0aciZcgSlHg0BnQppJ1YZ+LyXHWWc930znVbK1ZT1z7M1dq+/CAdY5MxqDBjCQCzAgcwIwmMj9zCCGctqbfjHDWqXISDNAsd2KNm4HPhhBWZI8pR1r798sxU/lLJwrIhjOGlMYK5BTrj2lpcbc1pDQVIs+YSnMMFD8W2x/IMwYhYxl0TtToL7MtLDn2GJW+DsLWhV1fxMQlHuoL+cCUghQYYtQoFj0U5Kkx0nUl26quG75wYH94sPo5L0zFwLWsufla7Jwodr5p+yHu5F8REgLMlCUkGHMTHepKvhY7AWLOqp2kCcNx2GUAdGmGTJvPeTNk2qlhJIWzTwYgIaUnZb8hlQglqz+A6RTMpftZ+H6JMquGEWeuU7GwBpYpYUbSaisdhcMrRyi1BeyoKtWZ2j0n9o59H32f/e8DNvW8D2pbv7dxgpiY1PQU0te//nVHA7Vf8+bNc9YPDw9jyZIlmDp1KpqamrBo0SL09PSMeT/VhGJEeYT9AgpaN9LxrWQ//s/qtKB/SkSNHhK+toYiy99Wnc6xE2o5ShPgKCgSZtSRVNqqflamgqLKdaN8/Ov8UVFhUVJqmYOCqSBf4zDlQrWr+M9dQdsx+vLE5Vc3MfrEJQrJLiVQ7ateqWkFBgAOPfRQbNu2zXk9+eSTzrorr7wSDz30EO6//36sWbMGW7duxfnnnz/mfeSMxeYLMd5UEjWiUsnDJ4ozEahEjeBGUKAgKHJK+RGWqxar/jdB+yzVp2JyCtZXfPij/50odw9+ZYyYuKR4vB7qrMp/9UzNTyHpuo6Ojo6C5X19fbjzzjtx33334dRTTwUA3HXXXTj44IPx9NNP4/jjjx+zPgop67oa9Uh9WIrJKtY2bF0lX0NHjjONEc0Egu3M6ioellwn/0rpfUirX8xJfqLK8YYYS9hVr4PlOrlUfP0rRZBFxNObInIqOEyf5NGj3D2QLwxhkxESSR6PUGoq5hhOzVtgXn/9dXR2dmK//fbD4sWLsXnzZgDAhg0bkMvlsGDBAqftvHnzMGvWLKxdu3ZM+5gTsi7N0xLFf63711Vy/JVOH5XqS2B76ebZVdUE9eVH9Zvxk5eF25j1He29uGjOess3xmonpUTGcJPX2TWVbDl+vxB1AsYfheTpB4CsYTr7qgUeg0JFGYK/9PaY2Jl+Kw0zDVB3CmQXG/coIlrD5NvU722cqJS4RCER4dS0BWb+/Pm4++67cdBBB2Hbtm24/vrrcdJJJ+Gll15Cd3c3kskk2traPNu0t7eju7s7VG4mk0Emk3E+9/f3j0b3Y4f6lY7iZl/MobIa/NaCsHbezyFbWJYMOwKnWMu8hKeGkb8dhzTDFjmDYXiz7KoP12FDgBkF9pcCK4Ft/rV/LTLGIPNmDSP/jyrbnybsvOkojB7yj6WE62tTyTVg++AUjl2hNUd15vUfRyXXjN33UGXU95kg4gRZYMKpaQXmzDPPdN4fccQRmD9/PmbPno3f/OY3aGhoGLHc5cuX4/rrr4+ii3VDKYfWIIIeuuXgf2iGKSRB68ppqz7A7EKI/orKdoQP4FpDOADNKkxobwu40T52eLCUbuiwISRy0g79ldB8iQ0TnEG3biJ5aSo59j4MIc3yAowhZ4U2J6zQ5qww95OzFKckN+sdAaajLmduf4SUZm4YxpwQaI0zT5JFu4wAU0weEma+GVsBcapnK2HQ6nip69Tz4I9CsrdjjBU9X/4CkpVcB/7Io7DtCMJPXK4LO5NLtTLqlVgdWVtbGw488EC88cYb6OjoQDabRW9vr6dNT09PoM+MyjXXXIO+vj7ntWXLlqr6leTxvkTCbv5Mee9fVy5Bcphvub9tJU6XQe3sqDXOvG5stiUj6MGqtrWnWuy2mm+dxl05fkuLqrzozHzZbXUl0R0DkNC40tZVdBxrBrP75j0uNckdA6Ar+WVMxcbqT8DJVRfpzDt+muKM7pwHe58hjupBY+kn6NyWOtd+RSVsWSXXDDFxYYhPFBIRTqyeuwMDA3jzzTcxffp0HHvssUgkEli9erWzfuPGjdi8eTO6urpC5aRSKbS0tHhe1WA+mKoSUdOEHVq1isZI5ITJDpSjhAaXFuS2LZz6CJYT6ncRIMMvhwXss2A7jFROSOeKHFeQnGLHU5ZcRHNu/e8JYiQkWGXX8XhCYdTh1PQU0r/8y7/gnHPOwezZs7F161Z87Wtfg6ZpuPDCC9Ha2opLL70US5cuxZQpU9DS0oIvfvGL6OrqGtMIJMA0n9dbIcdil7zqr1FsCqmcyJBiUweVynFkWf4UbnVja7rImhoJ+lsgx8ky620btEz9a+83sG9SgnFeUk7pY5RgjJc8rkoxx65we8+xAwX7DJu2sY/blVOoSPmvo9D+hbQpVw5B2GQlkFQSP9YylIk3nJq2wLzzzju48MILcdBBB+HjH/84pk6diqeffhp77703AODmm2/G2WefjUWLFuHkk09GR0cHfvvb3455P+MeQh10eQf5nfidV/3On6WiQPxygvZVrpxA2ZZPi+rbYlgRYvaUjBploylTLV45Zm0rpy2AnOWUa2e5zRrCaZvkDLolSIPldOuTZx/TUN4wo9akhCElhvOGkzY/K6QnOsI/NanKySr9EfBGwhmWbMC1DvqnxoKOGXCT3Kn7UuWq58VTUylAJsC850QWv45CrVj+vmJkcgjChqKQivPEE0/gnHPOQWdnJxhjePDBBz3rpZRYtmwZpk+fjoaGBixYsACvv/66p83OnTuxePFitLS0oK2tDZdeeikGBgY8bf7yl7/gpJNOQjqdxsyZM7FixYqK+1rTFphf/epXoevT6TRuvfVW3HrrrWPUo/olTIkZif6ubhuZHJ8Vw4YxVlBM05MpVxau47CccKU0HVMtOYZwo4cYY8io0TqWk6u9r7wwt9U4hw4JzpkTzaQxr0PsnpzhVTAgnX7lDW/vbadazhgS3LUuMMaQscKlzeNySwIAphKjjje3RtCs3SQ9crgylgUOucqYq59hyVbPA3fGymuFsj/7ldOilhRVjrK8fn87EkRpxiMKaXBwEEceeSQuueSSwMSwK1aswC233IJ77rkHc+bMwXXXXYeFCxfilVdeQTqdBgAsXrwY27Ztw6pVq5DL5XDxxRfjsssuw3333QfAjPw9/fTTsWDBAtx+++148cUXcckll6CtrQ2XXXZZ2X2taQUmLpQyp8eNMAuJSjnH7X+AlSJIpj881x/xwqT30Wg6tbpRNf7pLgkzIsfwazZW7SGnr6qSBNMiYhdCzAggZzVkhmFWbbbaakx56EvvvjUGpDRuRg8JgWFhTwm5uzOVJuk49Nr9YZBFH+620mNHIZlKEpyoI8+RqsclrdBr5sq0lRw1CilQwVHGvWD2yqfYMBQqJ/7tJNy2dn+ClF9/f+rt+0eMPnFRjKOoJl3p9meeeaYnAlhFSokf/OAHuPbaa3HuuecCAH7xi1+gvb0dDz74IC644AK8+uqrWLlyJdavX4/jjjsOAPCjH/0IH/nIR/Dd734XnZ2duPfee5HNZvHzn/8cyWQShx56KJ5//nl8//vfr0iBqekppLiQ4MxJXjYRqNYpM3pYwDs4kUZl+dFIWWCpUUkoc02qsgJ4ax/Z6x25vs9pzb2d2HJsPYWxcKdb/0NbnRayQ6jtdsyn/JQLZ+XPuYfKVTpYjRNusWuNBfytveuSqEU4KApppGzatAnd3d2eBLKtra2YP3++k0B27dq1aGtrc5QXAFiwYAE451i3bp3T5uSTT0YymXTaLFy4EBs3bsSuXbvK7g9ZYCKAWVMGBv0MrIpav6W4D/bqfvNHJaeCnP4l5EQjJkx+Rbsos3GtXy9EbWKnNYgDUTrx+hO2plIppFKpimTZSWLb29s9y9UEst3d3Zg2bZpnva7rmDJliqfNnDlzCmTY6yZPnlxWf8gCEwFGHUYhhRGls6T0CZPS9vYMToUf3B/pbOdOx7jbV9tXx/nUmVKRTl8Dc8+G7NAuK6DKKb7f8tbbfbNfld7upG/8pZWor5ScsEJx9mn0LEPl5yLIWZcgqiEr4Ti51zqc8UheADBz5ky0trY6r+XLl4/z0VUPWWAiIF9nHu3l2gUiU2Ks/9TpHuehpfhv+KcNgtwunIevz4dC/Wtvr2bZ5YyBw8xa65kCsjYcMiR0BiS46QTs+IdI9yGrwXTaTXKGrJBOyn/VmXXQEEhyhiSH5YBbOIp2pFCCmyqOIc0kdwxmxl+nnfLLLCfdpHR+h2YVzsw+G+r+FD3Ezjgc5G9TON4yVInxtg2mwPnXbuwT6z+P8fj9TNQqOSGhabV/FUVRUdrefsuWLZ6cZ5VaXwA4SWJ7enowffp0Z3lPTw+OOuoop8327ds92+XzeezcudPZvqOjAz09PZ429udSiWhVyAJDBMJ8r5FuXymlfnEzuBlzVfl+Z2Eh4XHI9UTnMPNhr3EzKy63lnHOLa9/ZX/K+5yQyBpu5XHbZ8WWO0nnaNA1aJwjwbnlhOtmzrXlZoUpIy9MBUADsxQU81+DxpG25GjMXKcxK9qJMY+/C4c71ob0Rl85x2W9EtzclnO3P47/TeA58F4Fwf/c/QSd73J+6Nrj6PRHHf/SmxMEUQb+5K0jUWDmzJmDjo4OTwLZ/v5+rFu3zkkg29XVhd7eXmzYsMFp8+ijj0IIgfnz5zttnnjiCeRyOafNqlWrcNBBB5U9fQSQBSYSJkoUhHqcfitIkFWk6K9u33bqr3DHEdWaCvEnk1Phyjqw4LbCJ0fCDSs0pKuMQJoZOjUG6IwXthXSLaho5V7RFDkM3v5pzEzvn3TkcEWO2x8h3fT9wtf3nBLSDQmkOJx8M2HHVWwM/GPnb1uYyM5+b3VB+exf58r1WVRY+DUTdv2UaksQIyUu15BdDqVaGZUwMDCAN954w/m8adMmPP/885gyZQpmzZqFK664At/61rcwd+5cJ4y6s7MT5513HgDg4IMPxhlnnIHPfe5zuP3225HL5XD55ZfjggsuQGdnJwDgk5/8JK6//npceumluPrqq/HSSy/hhz/8IW6++eaK+koKTAQkuBm6mh/vjowi/ukb//Jin0vKCbAAAN4vXVCW2WIp9/1wnxy1tTr1x4CCWj/qJ1vpsOWoUWdBZkxP/31y1DaqFdtjBZFep3C/tVv96D8uP6VugG4UVPHjV9s572Xwev/uwq6Rcq+XctoSRDlocH8I1DrjkQfm2WefxSmnnOJ8Xrp0KQDgoosuwt13342rrroKg4ODuOyyy9Db24sTTzwRK1eudHLAAMC9996Lyy+/HKeddho451i0aBFuueUWZ31rayv+8Ic/YMmSJTj22GOx1157YdmyZRWFUAMAkyPJP15n9Pf3o7W1FX19fSOuiySEmFCOvOVQbAqoVlCTwjGY+VmKUaz8gLkSoQcWlEq/FFJKJ4keAGcaKS7REyPFf83U99ES40HSmi4eKVE8L8rdx3ee+QMamhqrkjU0MIivfPD0Ue3veEEWmAgwpNe5spYpZbIPe2CEKSR+NbjcfCbwTXcE6dPFppGKtfWvK1YvSDUiyCLbFPS9xD79+2eWqcI7rRIsW93Wv15ICS1wmqe8MbCPFyHjEyrH5xUdNo1U7JoKcgouNTVUbBuCGClZCehCxsIKE6UTbz1CCkwExDEKqdIeF2sf5rMQ5BtUTI4/66v9oGLWOglzqkZVREylA07oNINb+4hbgvJSgjPz4Z8XZqZd13fFih5i7tSOsDqZE2aNoiTnkDCdd83U/gw5ywk3pZnm3UzejOlp1N1fdWafGbKGKSel2X4tlhzGrJpF5hQkA5AXZvyQzhnsogD2rcfusz02hgS4VQZBSjuSyh0DQDkmaTn7+m7YjrOulObYBoy7fU7UbRhcB2mmLCz3GlGXVXIdqtvU7y2ZGAvyUkKPwVU0HlNIcYIUGMLDSFWxooqKDA+hVUN+VeUF/vfSDP1lALhl0TCU7dSyAGoafAAwBGAovciqqfUt6wKzooVyhoGccB+Qe/LC7ZvlDJu3lKRhQ+0hkDEkdMvRNu+bAsoa0nFoFZYTsL3armFk7zTn881RI6PyymAKeC1ffiOKqlfb+1WtIWpzf/Zg+D/LEusjIH4/AwiCGE9IgYkADm9ejVomyCoSlRxPTo8QxcVcLQseuPbDlVvWCaet9Co1ngezpQyoMnRLG2CMIZMXgQqRvS1ghUfD1Ijs9X6jWpIzx4qS4MBgvjCFXUaYSovtcGsfj644yaoOhFoZCRBNa4tpqQnyf7GtEXYtJDsPjj8XTDnWC1ON81q4ShLy4y7wGglYVs4uSLkhoiQu+UPMTLzV9bae/eZIgYmAhMbBhYiNH0y1DwSm/HWmjIIiUCrYiSOziD9GudhTLU4IcQXbhu01pbm5V+xpqlKY1hN4+qNz5u1byLH6bzt+R2L1PHDluAuKXQb4qNjTRP49qZFW7nmwlxXtatG+V3vrjEoOQdjozL1P1DrjUcwxTpACExH1e4kQExe6qon6g6G+rRITCVJgIiAvpMc3od4p5hMx0nuCN0FadQMppPQkuOOozAoT3D/TaTepmXJ1zsCM4lYYT9CO9OZ+McsCmDJLGYb90z3FhlfCG/Xkmcrz9acUYcNfkRzr7wgNcp5t6FFDRElOmmkvElWEUo8VdhbtamXUK6TAREBcCoOpROVXEPSgcvZh7cTfRsI0a6qlEO3IGjvXCbfG1Mx4a7ax/VK44wxrJZ+zIn6ElNC47YwrIADkLEVB48ypJ5SHqYgkNA4JIGcIaFaelTxMhTSlm4bX3TnTu2kwL5ATEo0JDQxAc4IjIySElEhrHFKaeWU0zpBgpk+UISQSnIMzM+oB1rHYGXiFomQBrm9M3opUMuOflOOCWypAY65jM2A692rWSHLrOIWicDBrLO19BZ2TsPvcSO6BdXzfJGKOIYHEeHeiDKKsRl2PkAJDlIcs7yGnVkgu9bVx/V4KnWYdXw4JSGbnMIHP8iMhJSuIXoLvPWdukjopASHsPCvMCrU232pQHIYlwDlDWuMwpETGKKw8ze3OW384Z4rCwHx1fcIccJmnvABTtmHMVPbUsYLy3tNnH6Uy4pYLU2R59iO9x+Hfgdo2UMEtsr/4/RwgiNGBfGDCIQUmAjjzFtGLA5V01zMdAfeBJqVvisAXuVIsEsZ+bxYBNB/aaqp8O42+DNoQbhgzYFo2pNJICIkUN3OrAGb0kFq/aCDnBlQPGXlXcTJsU6vtqCvQnOBIclMLadBcBUPCtMjYm2ZFgGIDN8JJdRhknEHnpnVF49wzZWYIiZwSxmUWarQcfgPCeTTrJHDuJuqDlE7eHBXp+6suZ57/i5xb/3a+BRIAC9Fag6KRymlHEKOBVroJEQNIgYmABI9XFFK1FKt5U+lMWrF6RqVCeMPW6UVkMsaQV0oHAAFKgfJYTWpeOaqPSU6Ish+0tlXKluOEWAf0005kp/a5GNzXhiknpXI/IjUKyfvXpiyJJX7o0TQUUQskmFnZPQ7Y+amqlVGvkAJDEARBEDUIZeINJx5qaI2Tq2Hri+obUmwaIWzjoB/z9jK7Zk9Q7Z6iIqV3+1JygtY5X0f/NIaUyFlWDLutUPaRUNzx7ay6xeod2QnwnH0r701n4PJwx9x8l5fwHIv6119x2p/PxbuuuJxKb1dSuUpcHyZ3abnXS6lLoNh1OFJrG0GMhJwEska1sYlELUAWmAiIQymkipWXkLb2w7wUtnYcdKtwImgsp1W3to50IoxsP1t7XYIzcFi1gqTEsOInY/cnL4GBvLBqBJmZcTnM7LnDhoG8tH1S3Gy/zZoOnXMkGDBkCAwZQFYK7MwITNI1MDBkhVl3KGnVQrIjfFQ/Xd0yTesMyAqBnHXgkrljkRcSgpnHICxfHiaFJ8LIcdhVxtHWvWzfILuMAGdKzSLFgToIta/+7MVWT51zG+SU65HFXIUlxE+5gDAlxu5fDL5ORMyJi/rCInDipSkkIjaM9Oav6iP+yz3II6VYOG5BbSPpLlOjhdSHs/rgsr9sjmuuYq2xi2amNA5DmpYvxhiYlMhJYYYtaxw642blZpgKkcZMx1ldSuTyeQwZeUhIJLgOBoYhI4cUNCQ0HXkhYEhh3jg4czP7wnU6Np22XWXBPhbHSuMbTz8MhcfMrL7a42UrR45c640MkKOehzDsvob42lZEHVumCaImoDDqcEiBiQCdMSV6ZPwI6oH6wCpqUQmIKFG39aPOO6pJ4/xtDd8D118wMKF8r2yritkfa3rH6gCDGUFkWyh2K5FEQghkhVuJikEqVaHNMGg7eogD2JXZ4/ZJ5KFxDYYAhoXAYN5wFKgkZ2hNupki1GgmrpnFGu3EbnZ+FUOaik3WNw5MwgkFT1hRSHZCPNWSJRRlz7auSGsn/ig3tfYRD3DcLXauVWVS6aGzhWtNYaHXTLUUuy6D9heVwkUQNnodP9QnEqTARIDGGZgoXZhvvPBPSQDlR5XYVhTP4oAIH1NomQUAYYfrFpEDNeqncIou7IGXtKILgmQPG3mvBcPfxhOF5JWjRiEZlp+NmiSuGO7Yl45C8idEDPvlxHxt1LZhvjOlelosd0wUl7b/aIKuy1LbEES1JJmZsykOUB6YcEiBiQB/ReSJhX8SaYRSFAUhit6MJvV7OyCI+kdI018sDlMrNIUUDkUhRUAt10JSnSQr6SIr8h7wOvCqUUWswuicYpYCZqX1V/evylWTUDF4K8sO5w3PcWaNvNPfRj2BFFe29kXsqO+HfHKEEvGj+/oXhoQ/14s3ckgdg4TvV2GYJcWQhVFcNqW+1IVd955P9X2Bo24EVHItVnrdEkQp8jATTBLxhywwEVAr1pdiPgtBUy5qW9th1PO8VBUIy1HWiRSx0/wrMrj1i8b2x5BwI2eCHH0ZTB8OM6uudHw8ktxcnuAcmpRuvSPYPjUSOrNqGAnXQTcnBHJCIK1p4Ixh+9AAMiKPwXwOk7QEZjW1QeMcnZOaMGTkYUiJJj0BCWBPPo8k50hqWoGcwVweeWnuS2dAWyoBxhgmWY7EAhIJS0HJSwkOBo2bEU5mLSS37wxuHae8EGakFUxfIM7M42xgVltmKma2da+o07QybccshcyuJaWeL6l8LsRdakcXea4jqVwjgdsHS/RfTuVsG7QdQURNrdyzS0F5YMIhBWYCEvQwUX+QmMpM4ePGWaJYLhxnWnu55bAqLCdUpjim2sUJDWmGJENK5IWq0Hj3nbeUAGkVMnTqEXEOA6aVJMlNZWZ3LoeMkYdMpjBJ09GcSCEldGQMAzkh8PZAH1oSSTTqSezJmwqMzhg405CXZj+5MAs25gWQZxKcmZY1e2xsvxj7uPNCOBFDnDEYApBMgglT6ZDOuEo3TNyyUplWI1Nxy1vjZhaydCOObOVEwnVodsbSpxza5806Dc4naX1WbWP+M+u5HmTAsoBtSuFvX0qx9veHIAjygSkFKTARoFu5QcabkfagMAqpeESL/1e8+qBzQqatBf4hyUl3pa242GjMVBBs5WVISfs/bAhPFE5GiULakzfwfmbYWTecz6NRTyCt6UhrOnqzw8hakUo7MsPoz+WcKZ2+HEPC0kkMQyBjuA/6PXlhFWc06xY1J3S3kjSAobzb+6ytkMGORHKdYXNFEssxmFNhTtQRTCdepigeBlRLi3d7OwLKHn+1tECBBQXeqUQ/RRWJCD14o3QGJohqScTEKsFY9ekKYnKoIyJWPjA33ngjGGO44oornGXDw8NYsmQJpk6diqamJixatAg9PT1j2i+NMSRjcJH4fUlGLMdyLGNWrpUosKeenKgfZV3Yr3l/VaKkpjlyGGPIKvWFzJuBOwKceS9/jwePMqdm++TYMv2+KQXRNSGDbE/pMMYgS9xZyj1XqkygOiXBvmF6uhbhtR2DrwlR5ySZOy1NxJvYKDDr16/HT3/6UxxxxBGe5VdeeSUeeugh3H///VizZg22bt2K888/f0z7ZldPJkaJURzbsIzCBYqTk1UvIjkV9ifO1OdREXFEdYCvdWwfmGpf9UosFJiBgQEsXrwYP/vZzzB58mRneV9fH+688058//vfx6mnnopjjz0Wd911F5566ik8/fTTY9a/nJAwSjcbNaqP6JCha9WdSOmNyClw/i3RH78OYMvJGsIzTcKVdQzwResoUyaWf4fdti+TQcZwz0aLnnTeq/0GgLyR9/RN/VHmP6YhRUPVmNeS4HcIDLtdqFONzDe9VI7fiLrOGzFUfj2qMIKmn8aa8d4/Ud8YALI1MOVfDiyif/VKLBSYJUuW4KyzzsKCBQs8yzds2IBcLudZPm/ePMyaNQtr164tKi+TyaC/v9/zqoZa+CpU0gfP9EzAdAiDV2v3Ooea/hh5aYYE29l2Dcv/xfaR0ZhZGyjBmRVl5DqkFmTotf1KDIHBnIGBvEBWADlh1i8aEgbyUiDBgUaNoyWhoy2pYcjIYI/IwICAgIQhBbLSwNahAbwz2I+3B/rQn8+CAUjxBCZpKSS4jiTTkeIJJDUzoqhJ52hL6mhN6mjQ7LT+1pFLV4EazAsMGwJDtueuM17m5wQHUhpDSuMe5zLbT0XCHKOMMMsiGGCOU6/zUksXWAPPmTWeiobFfPOBjvwAJUZNuKfKVdf5T3a113QRsYHrSm1LEFFTC/dsonpq3on3V7/6Ff785z9j/fr1Beu6u7uRTCbR1tbmWd7e3o7u7u6iMpcvX47rr78+6q6OC1E9aGzFQ32gedYxyyoCU4GxrRC2YUJnrnLCmRu6Z1hRPbY8zVJqDEsB0jUOzqxfRBJIaKYTasYQ0DSOJNcwZBjIGVZoM9yH/PSGSRg28tidy6I5kXScdg0pMTmZhiEFdmaG0agn0JxMYSifx7BhoCWRhMaAwbyBrAAGcgI6E2hK6EhwjqRmRjwJKZG2Io8yQkBnDAnOHOUtqXEwZlpV7Mgiu2+cM6RgOuZK6c6554WZ+l9jpjIjpKmY2GOpKhi2nuScY+lWrLYjt/znz37vnD9befFZjTznuVonwQqXVyqHICYqFEYdTk0rMFu2bMGXvvQlrFq1Cul0OjK511xzDZYuXep87u/vx8yZM0csL8HZuJgkg/bov1TtB5v/b1CbIFkSPgdb5SEKuFYBu36RrqzLC+kZlyQ3CyEyxqBBQlMEpZRaQ2BAY8J1xp2kaWC67nHyZYxBZwyNLIFG3a1ZNDXV4MhJgGOfxmZnu0ZdR1MioVidzCOUMC1KqmUirTEw5kZFTdLd/uiQSOjuupQSPaWOnXmcAFNqHtmRVnauFl5kLAPPrTX+Em6OGPtYbTEMrEAhKXWdqNeGuj7sWil2zVRCVHIIolL8SSNrFQqjDqemFZgNGzZg+/btOOaYY5xlhmHgiSeewI9//GM88sgjyGaz6O3t9Vhhenp60NHRUVRuKpVCKpWKrJ+cMSRZbdRC8j8QWJG/boOQGVJlXVDNHW/TwuX+4VCjgMqR420bnPo7cL++NOH+fdp/1dpDnAXLKkdOUD/KaRs6BgFKjBNdVaYcR17o2uLXRjGlthyZpYhKDkFUSpLVt1ViIlHTCsxpp52GF1980bPs4osvxrx583D11Vdj5syZSCQSWL16NRYtWgQA2LhxIzZv3oyurq4x66eUtVlKIOzXrZv+v/QWap0iv3IQVMPI38Yvq1I5amHHUvICE/AV2affChG8T9cqUskYFFvn+WwncylDTuAxmY1Ltg2Vg3AlIvQaKrEtQdQieQkkQu5RtQTVQgqnphWY5uZmHHbYYZ5ljY2NmDp1qrP80ksvxdKlSzFlyhS0tLTgi1/8Irq6unD88cePWT9zYnyKOfrN/jbS997TThY+89Q08+o6p9YOvA8qQ0jHYuGZjJGms65dm0hngGRwlLu8klpfAsgbwvT/YAx5qw9JS0HICIEE49AYkJcCGWGgUTOnilQlQkBiTy6LRj0JBiArBISUaLCmnAbzeaQ0DQnGTOdjIZC09tmU0DFglQqQ0oyEsvtnuuSY2XrNmkWuH4m0xsDODeP3R7HHzpMnxxpLta2Eq4j4x1It3+A5j4qC5i7z+jB5J8hcghQ2jxxfW3+boHWkxBBxQ8D0uUuVW9BsHLHLs1Qro16paQWmHG6++WZwzrFo0SJkMhksXLgQP/nJT8a0D+NhfAl7SBVMPfjeq0YKO2rF2U75ZWIrHs46IRwNx5DwPGANK3MuA2AwiSQ3vzhJjUGzkslp3PT/GMjlrWy1DCJvhjwL5zGYd2oEGTILQ+SRleb2MinRqCec/r0z2I+sMLPy6hhGUtPcPmekFZlk1idqTTQ4oe6TNIYU59A5R2tStxxrzf5lDcNTPkAXVj5cBkgBMLjKKoNZHsHueoK502R2roli14amrBH+kwJpOfMyn3JZmETPE0oNr1En6DoIs6YU+xy2rpRcgqhFatBgToyA2Ckwjz/+uOdzOp3GrbfeiltvvXV8OjTGlPMwCcN+0Pgz2Hp8kH35SdSCgZASHGrkjDl9Zq8XMK0VecNsxxlDxgpVSnCzLlFG2PIlMkbeKoiooTmRRIOegJBAXzaLwXwWBoTzEN82tBsAMCU1ybLSuPleJOBk3W1JJNGaTMGQAjuGh5GTAoNGFhwMOtcxZABDhoE0NxWXnDQjrBJSeJQXBsViIk3HP415q49L5rbNW+24DLfIaczNXxDUTlov5rO2BOd5cdWHcpSI0bpxqwo1QRDRYFprq7TARNOVmiR2CkwtMl5RSJViW1KYz0wTlIgubPogoUTSGMItVOi3N+QlIJRxGcy7NYwMKZEVOWddY0LDJJ1bviEC/bk9Sp0gM8eLza7skKcMgAbuWB3akim0JMzkdXbVarUtV6ZqcpZ1BnDNyoy546PePJJ2fhjGwJlXwfNPzQQlNbTb2FFaqjWFWf8FnYeSDrjWnJHqUlPNlehcIyguJ2wdQcSBZFyikCiMOhRSYCKAM4YUNxOU1TrVXsv25iOZV/UqRd6x8nzJLL+SoO3MPnj37a1vxDzLRJF2Qfi6UKCkuNsXPsLDZPvXqMnqpNLAL6Is5SVk+5FSTEzJ/kSze4IYVeIUhUQKTDixyMRb60gpa9ICI1VTiu2QW2W6eREmx7bEFNmF52vka6NaavxTWP6vX9gRqD4i0ien0mMvmJ5znJqLTeWUKVdxjq5k/8XkwPunaorJGWl//RY9ghhPcjI4YzURP0iBiYCsKO6sOR5I6SoRds8kiisffmsD4L0w1No/EkAmLzwRNGodHkO4jysNXlPtJE1DwmctENL0cdk6tBvdQ7vNiB8AujLF0qDpmJJscKZ7mhMJTNJM46HOOJoTCae/fdkMdudyjt9Mkmue+kvqsWuMOUn5mLVPZwx945ExvM67HgWLea1C6tj526r+QqUo93eTeW7NN9Xel6Xvb7H1lcgaybYEMVpIeGuS1TJUCykcmkKKGaoiYX/2O1AWsxwAwYUHGQDGmWMF4ZbSwZzPHEJKDOWFow7l8tKz75yixOkw6wvZpsukEoWUlhxb9ww4SeR254awJ59BRhjYPgzszO5xaiM1agnMbpzipOifnGqAlBJJS3kZzueR5Bycc+hMQ38uCwmgL5fFYC5rpuqHmYmygSct/x+GBo0hyRk0bqoaeSFMh2POwQyBnDS9QDw+KMw8xrwyuhosh1xLjjp+/rFUb5hmLajiN1DnnChKnD2W7i9Hyw1aNVx51njlFbtGKvFnCbKKlXLejcdjgphoxOW6NMuyVC+jXiEFJqYEKStBDyn7QWhbJHhAW/tB6dRmNgQ4c+scacJ8UDcmNBhSwhDSiULKW0lIGnXTYTZjCFOpsPa5xzAwaIVKN2oaJukaZjQ2IycM7Mnn0DmpETrjeG94AAJAe7oJhpTYmd2DZj0FnXNIKfD+8B68NzwICYnJyQYICQwaOSQYR0dDE5oSCbQkkxjM5yAl0KjrkAAGcjmkNA0pTTMVECGc+ka2Upa3HHYn6WaemiTjyFsKme10m5d2DSNzrISS78ZOZGiPqSalcxIMO2rLsTiZFht/iHTQOZGKg4yQXqdi99zCkwcm6DoJukb8n0spMtX4xcTlYUFMHOr4mT6hIAUmApJWFNJY36iL7Y95nog+B1cnVEVabVlBJmEJV3kxt1PeA9CUKCTd2oWdWbbBqhkEAMOGcJQXwKwDZKMzjtZk2pGzd7rJ6Y8GYFq6yZHTl8ugZ3jA2bYvl3Ud0xjQoLv1jeypJbs/Lcmks05nQEKpaTSYMzzHqSs1i5ypJWt8Etytd8QAS7Ey5eR8517NxeJ3euYBzstqnSl3n8Hnl/m1HbiKDHyrKlUeivkajfRmH2QJIojxhiE+tZCimAKiKSQiFMYYkjUYheSNqvE6n1QSRWRbBBDyN2iZP9cMV9JiB/31p9IPqllk98dGs0Kk/XWTwvrqpPVXZSoKS6njKl3LKHBx2XIDtwvbh29lpberkVpXqpVPEONBgsUnMoeikMIhJ94IEFLWlvKihH04Dqw+B17V8dbRD4p4gEoEb18ox/tXrYIqrakne53fodiTIt+3TPM9AlV7hyFEQR9MJ+bi/bLXqVKDtlFfxeQEjpcy/uoxlZKjtimQiULFyH/aPM7KgVKCKTatFCRH+v4SRNzISopCqhdIgYmA8fJoD3OclHDr+TgRQsqD0k7XL21/DeULzWFOoUhrccaQZq0ia6ppT94wt5cSw4bEkGHWH8pJgfeHh5wMuWmNozmhW74eEtuGdmNXdghSSuzM7MELO7eiPzcMQwpsGtiJN3fvgCEEho083uzf4bRlABJMM3+NgGFqahJaE2Y18bwU2Do0gKylyAzm89idy1vTYBJ9mQwyhgEpJXJCYijnJtPTmTuGqs+PfZzqeTVrLMGJksoZwmnrN4Yw5p4cvxJgWNNNQYqcq5DIwJNrTys5MqV72pxkgr6pwHLxyPVtW2xdpYoSQdQKtZj2IgjbAlPtq16hKaQY4pn6QOFDRP2sfk8Z8/q2SOmdnmESVk0gc6onmzPMVPqMYVhxdmWMIWsYHnnbh/qtfTDkhIEGXUNrIo0GTcM7uV0YyGXQoCexOzeEJ7rfBGdm1t0tA73WPjUAQF92GAISOSGwI7MHzYmUM/U0LdmIRj2JtJ6AEAKDedPtOCcEeob2QGeaMw0zbBgATGVjWEgkDbfvuZxwQsc5A1LcDKnmnCFnCOSUMTIM6YZ8Q4Irvim2gsOYOcvMYctkjqJinyRfmhuPX5Eanq36nZhuMVL5zAoUCwR8JsdagqgPyAcmHFJgImAsHwhhyorfKuovJmjWJoJVAkB4HHdti4xlZoBkbkp8IQRywrVcNGk6GjRTWdiTy+G94T3IS7P1YC6D94Z3Iy8F0pqOrJHHm/3vQQLYO90ECY4hI2dF/SSdr1Yj4zhqaic6GlogpMRbu3egNzuEPUYWDAyT9CR2ZIexIzuMRi0BCdefRmMaEpbywqREczLhVKPuz+UwbAjkrP4lmQZmhUhLmOcuI8yRTFjlCjTmWju4ZYmy6z9xZoZI29WzVaXCVkSE79edel5spcmO/PIrJKq/kb/ytMc72Jbn+v66Sg/CKeda9StTpZRmgogT9ftIn1iQAhMBCW5GooQV8BsPPGn04XXm8ltiPFYbeC0GWaVoYoIzTNI153N/bthRDoQU2DbU56zr2dOPHZlB5/NAPufUJfL/rjiodRqmpZudz325YedBrjPuaT0shFsyAAxJrsM+gqZkAmnN7B9jzCwcaUcA+cypykxPAYovcEHUgrQaVHITtHfrT3LnVwycfQTM0UtfG9Uve6SOu/69+KfDill17HX0ICDiBkd8opDIiTccUmAigDEGnUlk6+Rnadhh+B/b5UxpqFs773zfKc2jEPi2DImyYb53YV/Vih/y/v2qsdHhXQxdFxptVGM3myjHkyBqAZ3V3vesGEyJ3KxGRr1CTrwRIGTtKy8eB1FU+MBV3hu+yJwE15R2zBN5pFvrbEuHUCpK+yOBBvNZpcghg65UkBZSer6E6sSL6qQMmAnnPMUdlb6XCjwIq5WkOt6Wuh0USPFHD40wAiLsRlSJQ21Y/8kxl6h3srIwNUOtYt9Tq3mRDwwRSq3W1dBgRSPBDjRyH+46ZxDSzjBr5kbIS3NqKWll2bVDw9sSCWSEgSFDIMHMStF2FtqpqQYkuYadmSEkNI7j956NzYO70DM8gCOndCLBZuCZ996GgMQH9pqFgXwOf+3bjs6GFsxt2Qtv7t6B3uwwklyDkNJx2D2krR3b9vRjV3YIU1ONaE2ksTM7BAGJqVYm3v58FmmuoSWRxLBhICcENMY8xzkllcRg3sCwYSClcaQ1hqxh10kCAHP6jzPTrCykXX7AG5kEwDMVpUNaJQFc5117nDlcRcCe3vFMFSl5X7j0TT1afi5qXhxYcoN8YFTKvU2Vmvqp39sdQZjkhISm0ZUed0iBiQnFVCRbMbHfexLCWZllmbLO2Y4xcEjodgSN5QDLpWtilcx8tCY4h8YZ8nIYDbruKC+2nOZEEkNGFg1aAi3JNGajDcO5PZjV2IaUlgADYEiB/Vr2Rl4INOoJTG9oQWMiheZECrvzWbSnmzz9S3ANnZNa0JhIoi3ZAI1xJDUdAhJpy7KT1nSrbxwJjcMQZikAjwWGMTTqGlKcO064nJm5Yuz7l1lryZpvthQK7hyfK0eFM9MVWFUG1HH2KB+sMEmf5zx4vHy9fS/mPBvmPxO2zk8lbYOoZluCIMIhH5hwSIGJgNGMyiglV03I5ESq2J99jsU6pKcooJoCX0iBnN1YSuSlYRU1BPIih97sIPJSgAGY27I3JukJAEBvdgjPvrcFfblh8/PwTqzr/isyIo+WRANmNk/HO3t6AQAHtLQjraWwx8hBYxzzWjuwx8hBwiwd8MG9ZzrTTj1Du/H24C4IKZFgHHunW5yoqBTXwMGdY2vQdAjYDr9Ac0J3jjNjCOyxilDazrj2MZtWFlVhkB4FQUVj0ikqKaVrfQmC+/O4eJKzmLWR1PpUKkyxsoRGnAWsK+aYGxZeHSYnjKBjJ6deIi7E5ToNCzSoREa9QgpMBCQ4Q15IGKWbRkopX4qwKKS88CZOyymNc1KYRRotdmYGnLIATXrKUV4A4K+92x3lJWfk8cTWl5x1w0Jiy+Aux+qQMQQEcgBM/5hBI+e0ndHY6pQFEFJi08BOZ53OdY9vi5Bu2I05deMeV0rjni/sYN4bGxZmMQgKabZRrU4l/URC7hij5XQW1ZRQNTe7er5REvWDBnMKnYg/pMBEAGMMGpMef4lap5Kuetr6HVJ9DrUhTb1TWCHrKiuLGeZyHC1RefOHyqljcy9B1AJajKKQyAYTDkUhRYAxClFIVYmTyqyFWeSnoLYPd1bLgnpJqqXGrMSsOe+H8zkIKZxXW7IBgDkFxRlDg55ydm5Iw8pSa8obNrKO3JwwzERuVtu+zJAzrcIkkOaubp2XfitKYRSSdKa7pDs9oxynejzKMPnWFRlPuFFI0p80J6htCRONU68pXExFhO2yksgiikIi6p2sNEt6xAEG5oRSj/hVxwoMWWAiID9KX4ZSUlXn0FL+ERJmVJHGzC9FVpj1kMAkDCkwlM8jrenQmIYGTQPA0TM8hJwwMGzkkTGyyBg5bN3Ti0e2vIhjprRjr1Qj/v3l32Pbnn7MnzUf/dnd2LWnB3tP2guT0204ZZ/DMLd1Ou746xPoyw5h+1AvhvJZ7N3QCkMKvDu4A9MaWpHWEtg82Is9Rg6HtLUjL4U5TZVnyEuBBi0BBoYk4+CMoS2ZgpRAb86cgsoJw1GyckJgd1YirZuqi20pNp12mZNZF/A633Jm+s/kpTv1pk4j5YTpA6NZP4iYTwGx/aBM/5bi586MVLJvKdF7T0UlrcA3J+BzPB4BBFFITsqCIrFE/CAFJoaoUUcFdXcg4denOHNLBdi+L3YZASkMJy1+xsghIzJIpyZBYxwv73wbe4wcOidNhpQSA9khvLenH7uye/Dku68gJfN4ruc1QEpkc7vQN7wLg7khzIDAUa3TcNSUWZicbsaUVBP6skMAgOG8WRMpa+RhSIHmRBKzmqage2g3erPD2JXZAwEzvLs5kcLkVCP25HOWIqMjqWlIazqElNDzwrTAwDQLJzlHTpq+SI6DMmPQAKQ05nG8VW9dOleKpglvXhlTAWJOOLodIm2HR6sPcjuc2i4TUIjXj0ZdxvydGgXCItnKaVdqO4IgooUmkMIhBSYCRuvXqD9sFoCnfo4w5zWc9nalZBvTquBGzkjASeVvP2A5S0FKiTf7d2BHdg8A4NVd3Vj/3pt4f3gAAPB30w/EGTMOg8Y5hJTozQxiSroJAHDZ0ecBAFpT5ufr//RvePRv6/Hslhfw87/8DsfM/hAGjDw0pmFKqgn7trRD4xxMAsdPm405zVMBAAe17G3mZtHMSzJr5JHgZn2jZj2BbUOD6M9ngTywO5dHSktC5xqklGhMcKd8gHtc5ogZVo4XxhiEEFbdIy95axw5pBNODSmhceaJ2ioIU1fW2VYuOxLMH9bs1jeyljHve2Bk11DQNVLsczH8DsvF5BBEvRAX3wkq5hgOKTARkOAMhpSe4ojVwnx/bcJ24V9nKy8AnIrJfvmAOQVjKy8A8Lfd7znKCwCcPP1AaNyekmGO8gIALclG531/ZhCP/m2987mxYQp253NOH2Y07+XI2buhyVFeADdrr42tyADAkJF3oqDMtnbtIwadM0d58R8XYFbWthEBqqZnTNSQal44XupYqoRNtwS6GNsRVFXeV4pdI54I7grklLucIOKMDu99gYgvpMBEQEEyspjgWAWC1qG8X+BeP5xSnhPlySlzi2jklNnFyKIW6L5JEOOKbZGNA4xF8EMnHoc6IuJiSatpDDE6tZCk8rcsZcL32faFcRQMXx0je1mCa2jQ3NwuezU0W5l6zddrvT2WPHP+JS8MGELAUKpUCykwSU9j7uSZAAAOhoHhXtNPx5LTr1Sm7ssOI2PkA/ujLpNSIqV53e3ynv3CiWYqJafUjy7v9sXlqMuB0vlkypUzEvzXiPQtJwjCS1aOXuBF9LCIXvUJWWAiIF/FA6gYxR5E3LIwCFgOppYDg5CmWVSH++U0nXfN/DQ5QyAnJHRmPsgHcnkAwF7pFDhjOGxyB94bHkBvdhgHte6NE6fth0e3/RUMDPu17I1de/rw1Kbn8Ye//i8eeOGP+NKp/4iD2+fgzvX3w5ACv/rkD5FOpHD7GdfgkbfW4sl3XsDZB5yIQ/eei9Xvvoq8FPj7GYcgYxh4rf89TG9oQYJr6MsNI2sYeGdPHwwpMLd5LxhSYmdmCFNSaUxNNyKl6ZgxqRm9uQyklGhNpgAAw3mBBDcT1xlSQoIhkzcgpem0C5iRVwnGkNbNdmnNdWS2k1nlhZmhmAOQlm+K4zdjnVshYZZaYK7PC4etGLnnRJ3WsX1L/NFidnkHoShAdv2mSq6kYteIupyihQiikLyU0GPwYCcfmHBIgakRynnIqJErjl7N3Kkb5q52MKTEQE44D1JDmlFKps+OwLt7+jFJT2BysgEDuSG81deDKal90aQlsavnLfOhP+NQvND9Oj5z31UwDAEhJd54729I6Bq6B97HrqE+/Nuf/wtzp87G3+/fhXl7H4ic1oD9psxGczKNw6fOgoBES6IBed3Afk1T0ZJMgwHYlRnCjsweDOVz4IwhY+WHEZAYzOehZ7OYpOvQGUOjnoSUpm+PhFujycwk7C+N4B2DrCGgc7fuk+q4yq3wakifAiK9EV1FrRxSes+JJYD5tiv//EZL/d6+CIKYyJACEwH+qsWVUtGvbqWxkN7yBXkhPWXi9xjCs75J507kTlZk8c7ATsci8EzPm9g8uAMMwG83/glPbvgf9A/1AwBuevQODOWHkUom0JxqxG/+8RYc3nkQDCFw8PQD8bXHbsUvX/wdAOD+TRuQTLeCAXiy+3Uc236gc3xv796FtpSZ+G7rUD9arCR4k/Qk9k43YZ9JLU7K/gTXkRECA/kcBvM5pLQE7EfxsCGcaCpYuW1grdUZ0GDlgGGMgeXNMchLIG+4bQHznLmRRJa1RKrrXAsGZ4XWFeecOP+7FpQgx9qCaB87FIlVZydRZZO1hSBKExffiSgmgOr5B0xNn8fbbrsNRxxxBFpaWtDS0oKuri78/ve/d9YPDw9jyZIlmDp1KpqamrBo0SL09PSMeT8TnCMR8VUykgvX70uhKi86Z86UCWMM72cGnYfdkJHF5sEdpgwAm7tfQ/9Qv5VjRWJPdsiR/YGZh+PwzoMAABrn+ONbTzsRQhpPIJludeRMSbd48qE0JpLOe7vukd2ftmTaLZYIICPceGfOuEdxK2YSZQxIaN5IIXUMSo1ngS+LpRU4OV+UqK6wrQOb2NsynxymqkWVoV4jzLesvme+CWLkJBiQ1Gr60edQdRbe0HtW/KnpszhjxgzceOON2LBhA5599lmceuqpOPfcc/Hyyy8DAK688ko89NBDuP/++7FmzRps3boV559//jj3uk5QrvnKahMR4dTvzYQgiPjz9a9/vUABmjdvnrO+HMPB5s2bcdZZZ2HSpEmYNm0avvKVryCfz0fe15pWYM455xx85CMfwdy5c3HggQfihhtuQFNTE55++mn09fXhzjvvxPe//32ceuqpOPbYY3HXXXfhqaeewtNPPz2m/cwJgdwIn/Hl5nVxo1Xs3LPeLW3HULWtWu8oLyTyQlh+HWYNIyElhBBo0JJo0UzrCAOwz7T90JBoAINdU8jdz5/ffQVv7dgCADCkwClzPuhM5xgih92DOx05u4Z3e6wRu3PDjiXHsGop2fTnMs50FqRESskLI3y1kETBsbvvbQfmoFpI0tfWv3E556J4xFBhBFMYpBISxPiQs4IaiOIceuih2LZtm/N68sknnXWlDAeGYeCss85CNpvFU089hXvuuQd33303li1bFnk/Y+MDYxgG7r//fgwODqKrqwsbNmxALpfDggULnDbz5s3DrFmzsHbtWhx//PFj1reRRuSVq7zYn/0+DpwxMMsPRgCQpsrhKRcghIGszGPYyGLbUB67hvsxmBvCY1tfNR/2727Dpm2bsHLdSpx/2sfw0Q8vwvzDFqD57y7GaT/6R7ze8zcMD2eQ0HVM3qsFe/J7cOa/XYyLPnQh5k0/EA//7Wm0Td0HDTKJSelmdLTtg45Jreic1IoPtR+AzsbJ+N+etzCYzyKlJZAVebQkGqBzDo1x6EyznGh1vD88jCY9YWUTtqblGJCw2mjWVE6DrkFIiT0BmQOFBIYMiaTllcsYwKUaEaSMH8wF9nK7vpHjx+KxQtnZeG0/F/++mfJ/OFEpL36fGoIgysMAkCjZavwxb4HVRiFVjq7r6OjoKFhuGw7uu+8+nHrqqQCAu+66CwcffDCefvppHH/88fjDH/6AV155BX/84x/R3t6Oo446Ct/85jdx9dVX4+tf/zqSyWSB3JFS0xYYAHjxxRfR1NSEVCqFz3/+83jggQdwyCGHoLu7G8lkEm1tbZ727e3t6O7uDpWZyWTQ39/vedUKxXwXgpxCGTMzxnJYWXdhXuxJjSOpmWHDOudoSuhOLpUk15DWEkhwHU2JJA7dawb2a5sGXUvg2Gn74aNzjkZnYxua0424ZsH/xb+e/gVMndSG+fsehR+cdy3OPWwBEloCjckGNOgpJHkCM5qn4ctdn8aFh56BlJbAUVNn4cIDjsfs5r2Q4BoOnzwdR07ZBylNR1MihTnNUzA13QjOGCan0uhoaEbKKhugZgxu0DS0JZPmsTCGBl3DJN1WZhgadIYkZ2DM9PFJaWbqf/9YmXWSmOPAq1nt7X1pzCr0aMnizFVeGNwaR0FnSo09chShUfKHImWFIIiR4n/mZTKZom1ff/11dHZ2Yr/99sPixYuxefNmAChpOACAtWvX4vDDD0d7e7vTZuHChejv73fcP6Ki5i0wBx10EJ5//nn09fXhP//zP3HRRRdhzZo1Vclcvnw5rr/++oh6aD78RpILJix6xL8uzNXTDiXWdW5NcZitGqwHv5QSbcm0ZUHYGxISJ04/EAwMQgowxvDNC76CVCLp5CMBgHOP+nsIKfDPp16EpJ6AIQx89LDTkc3noGs6pJRYOPs4wOojB8PpMw5xIp1sOXulzHIDMya1OjlTWpNptKebPMqKyiRdc/qedI4LjnbAmJnzJqF7M+/qHjGW1caSo3HunQIKGEy7f/ZftSnzrAsVU9G5DSNMaSGFhiBGhhaTL08UTrj29jNnzvQs/9rXvoavf/3rBe3nz5+Pu+++GwcddBC2bduG66+/HieddBJeeumlsgwH3d3dHuXFXm+vi5KaV2CSySQOOOAAAMCxxx6L9evX44c//CE+8YlPIJvNore31zOYPT09gaYvlWuuuQZLly51Pvf39xec3ErQuVnFeCTZeMMUE8+6Ehexe5Eztzigr+igXZjQ9m1hjEFj5hM/ZUUIeeoBMQbONEjLx0Wz/FKSesLeqbMfW7ZuyVPlMLdDgf2xUZWewuNC0XUFtYlk4XAVaxtEWFt3XUkx5Z/bCojJfZcgapYEg1OTrdaJwvJqb79lyxa0tLQ4y1OpVGD7M88803l/xBFHYP78+Zg9ezZ+85vfoKGhocreREs8zqKCEAKZTAbHHnssEokEVq9e7azbuHEjNm/ejK6urlAZqVTKCc22X9UyVk6ZpdPOl9+TalLYRykjSjmWtIjaqVnsyO2WIOoBJ1hgguF/5hVTYPy0tbXhwAMPxBtvvIGOjg7HcKCiGg46OjoKopLsz6WMC5VS0wrMNddcgyeeeAJ/+9vf8OKLL+Kaa67B448/jsWLF6O1tRWXXnopli5disceewwbNmzAxRdfjK6urjF14AWsNP0RfB/UDK9q7Zxy6uc4bZV2hk/G7uwQADOqZ/eeAQwO73EifLp3blf24d3Pzj19yFl1iwBgx55eZ31W5JEVhrNuOJ8r2r+8FE7dIgDIW7lenH4rfRe+vvujgNR9iIBlQfWf/OvNgKeQsZSKnILlyrlSXh45Aev8y4otBwrlhW1PEER55CWQi0ktJBbRv2oYGBjAm2++ienTp5dlOOjq6sKLL76I7du3O21WrVqFlpYWHHLIIVX1xU9NTyFt374d//iP/4ht27ahtbUVRxxxBB555BH8/d//PQDg5ptvBuccixYtQiaTwcKFC/GTn/xkzPsZZUCe/+Hl/BVW/Rzrs65ck3khYEi3xk/GyCIvBQbyWQzns9i8ezte7d2GbXv6wN/eCmPXLty98lfQuIaLFn4cG7e8iT88+zj++fzP4qbL3FC3O9b+Bi9tew3/+cIj6Gydhs99+EI8/e6LeG3n2zjvkI/giOmH4Ln334GExBkzD8ewkce2od2Y0zQFx+w1w5GzfWgAQ0YOOzN7oDOOGY2TMWzkkRUCjVoCaT2BjGEGR0/SNCeCilvOt/ZxpTXmZOo1j1taZQcAzsyQaTtxnWY5mNjjpyljx62Qc2mJ8oRaK8qSusyFKcvt6T0UUEz5CCKsbalII5pSIojKoSDq4vzLv/wLzjnnHMyePRtbt27F1772NWiahgsvvNBjOJgyZQpaWlrwxS9+0WM4OP3003HIIYfg05/+NFasWIHu7m5ce+21WLJkSdlWn3KpaQXmzjvvDF2fTqdx66234tZbbx2jHo0eUhY+yJy8Lr6/QkpkBKAxMzX+YD6PjCGQ4BoGc8N4dsffwMAwp2kqfvfMI/i31fdi/wMOw/HHnITD5xyCxn0Z1v/1OTyz8Tn86OG7nCfx6rfX4+yf/l98tutjOGb2YXjgrSewrW87uKZBphrw+Pa3MJDLojXVhMP22hf7t0zD1j39eG94AK/1v28VRDTrGb3W9x72SjeiWU9hMJ9HTggwxqFzHRlDQkhmObFy59eQzhgSnEEAMAzT0qH+UspL89htBzzhGxv7psTg+tfYJR7UG5Z/nAUAVqZJ2e94K63/HDefsqRURpjMYNWq9Pak+BBE7ROlE2+5vPPOO7jwwguxY8cO7L333jjxxBPx9NNPY++99wZQ2nCgaRoefvhhfOELX0BXVxcaGxtx0UUX4Rvf+EZVxxEEkxNxMtBHf38/Wltb0dfXNyJ/GEPKqkySfuXFDsf1Tq1Y+xLSeRhLKdGfyzrbbt3Ti/Xvv+U8nH567/fw6hsvAmDYv2MWnrnxIaQSKUgp8d3/ug3X3/c9Z5+JOW3gCQ7GOJqamjFnzgHgYDCkwPQp+6F50mRIKTE13YRvfPDjSPIEGANe638fL+zc6jjgzmqcjKQVEq0zDS2JBjeayArfti+5pJKsLqVxNFjRRlJKDAvp1CcCc60kdugzUxyIVTRmlkxwp9GssYL3oV3sO13sq65GIcmAVpXcI8qJPhop/m4U2w8pMMRERmduaZVKqfZ5Uck+NmzZjKYq9zHQ349jZ84a1f6OFzVtgYkLZg2fkUUhASEPGSWUN+gXvvAl+d+VGfA8HN/a/Lqzh8NnzcOklOtB/uTL69wNdQ6e1KyWEpMmNZoZe60Q5IZUk9OffRqnokF3zYDvDw866zhjSGm6ItZVUNSIJ78SAphTPmoUkrSS0AFKxE5IVJD72RfNZI+fsr4YwasCIp8CrGWVMlIlxm+V8y8nCCKcJDPzZ8WBKHxYqt2+liEFJgLM9PyRSkR5j6SCWGGvJUd5WgvhnfXlahih34k15NEaWhep5BjYDaL4QpU7RmNEjXWHIIhgDGlOGcelyGE8ejk+1HQUUlzICYlqylQFmf3VqRFVv1CLqHIAaSUB3JymvTDVShoHABf/w6VoSpufV/3lf/HAn//grPvMmZ9AwyTTIsMM4KDGmY7FxBjKorNxL6dtg8whrZm5Xzbu2oqXdr7jrDugeSpS3NSDBSRyhuEcT07koSm/dKQ0nF8DUvkfADLCVY3M6SdlOwRZX4K/1n5FspIvfylfEzXyqBrG0k+Gbn4E4cUAkI1JFBIRDvnAoPo5TTuCphpMPxjp++w6n9p1gOwHkmFF6XDGkBcCWSGQtsoFbBnshcYYOie14sV3N+KEGz8OLZ0A0zlOOOBoHDBtJh598ynkDQNfPfYyHDn7EBy53yHYvGsbnnxrA84+9MNoTjVi/bZXwBjDcR0HozezB/e8/hSmNLSiQU9hduNktDc0m0UYpURLIoWWZBpTUpOQNQzszg2jNdkAjTEMG2ZskK1sZYRAgnFonCFjCGStCCvGGJKWA5C0BoExq0wCY2ZoNVytW1jvGWMe3yBnnJRZJG6Pny/DLuAaTwrXMUWpCj5nzG0K9W0UXyq/nDD/llKKSiVtCWIikNZG9vt9LH1g/vzOO2iuch+7+/txzIwZ5ANDREfBA44BzOdwaj90hfSaynJCImOY0TicCfxtYBd2ZvZgZlMbunvfwe3rf42UnsTlH/wkZk/dB3+5/v/hoZcexYMvrsaSExZjztQZYAxIaAl88u8+Co1x9Gf34A9bn8ez/W+haetUHLHXXLyxZw8YAzr39KG9oRmfnXcytu7px9uDuzC7aTJaEmkzqy8kZja2gcF0aB7IZzFkGNDzOTRoCWQM86g0JgAw5AQguURKAgluRh7lLAuMbbHJS/O9HTYtpHQccu3xEpaSp0GayhxcC4xt+BFwFT0gOEzaUVRMU4//tBSFsWBlpRrflnKXl1pXTVuCIGoD9UdrNTLqFVJgIkC3HsDlEhbWKmHV44E5R8uldB7GjDHsyRvIWaaGrCHw4q7NMKxkdP/x0iP4zxceAGfcerBKtKSb0NrQjCUnLcYXT/40DGGAM46bz7nO3J+UGM5nseTxH2MwNwwBif/52wZsHBhQrD0CGuNo1JM4oGUvzG3Zy1GyZjW1OXIAia17BmBYVoz+XA454apeewz3WPJCIq1ML6WsX0O2BSShfOuElFALTxvwKnsqtgJkyymV48WT18WW63MethepfyshyHIympFIBEGEk4iJEy8RDikwEVBtFBLg+nn4I278vh+qnpSXeUd5AYDtu98DZ9zJrrtv2z7O9nYdI92KErIjdRhjGMxnsDs35MhpSEwyFQC7GGO6ye2P3T+175YsQ0pHeQHM4o5qRJC6DWflRRXZY1MMfzRTmJxS+DdxppaUfal/y6UghHuEcgiCqJ4kK6zDVqtQFFI45MQbAdI/vVGxAHie0lLJVR/somSHBnsvTM2qAm2TE3lHmQmSYy/TfYXNpPRGLOWFKEjR75cR1J/AXhdxuSqW0j9kE1emd6OAYy3lnluaqKwlZHUhiPElL4vdV2uPWiglUMuQAhMBOSGdFPYVETANoiavs9flhftQZhAwrJDolJbAnKapzuV5xIwjMa1tH0fWN5+4A+9ZdYt254bw5+2vO9u+M7ATW611zclJOGvOCUjY0UQig46GJkfOUz1vYdgw46yElBjIZZ3+5IRAzpLJAMsvxkTA8CSLEor5yJBAVlWM1GOW0qlvBBRepP6vo6GMT8FYWrWL/OsCkcHKkgz469M5yyIqOQRBjBwBikKqF2gKKQJG6hPhD81VH9qGlFatH/PznnwGWWEgK0xVaZ+GZuicoTU5BTsyffj9lpfw/vBuzJlxDLIQGM4O4s873sSnf3c9PjjzSGzctQVDRhYHTpmLmc37YPPgTgDAKZ2HYI+Rh6Y34zOHnIsZjZNw2JR9oXGOtT1/Q39+GJP0FJ7b8S5mWjWMBCTSXEdaSyJnWWtaEmnLf0fD5GQjElwiremm307OQF5Ky7fHjagSEshICTV7DYf0zOUwS6FjsCKOYCahEkrUEZgVrSXdHLlBv7D82XvNqCP3nARGGpX4bG/rj/IJ+1xMbv3+TiKI2iIu6gtj4ck3y5VRr5ACM04EWV2cYoMw52jTzDR3DhsCDBpSXIOQWfRlB/HCcB8maUm0JFJ4eVc3UloKrUmBvDBw8gGnIi9yePO917BPy3TsP/UgpJOTkcvvxkXzTse0SW34wzuvYNvQbjToKehch8Y42hvbkEqk8N7wICYlkpg2qRV7yRbszg6hKdGABr0BGs/BkAb2SjUgqenYnc0iI4SpaFlfFFNR4cgKCY2ZikvCihrSGHMceSVc64qp/Jgh06YyF2CdYIqixwof+EF+JoEKh6ogKRuN9KYWhTJSx/cYgiBGDMUhhUEKTAQkOKvIJOn5dW492FWvE7tYIWMMUgjkhARnZp2gvuwgBvLDAICd2T34/ZaXAJhKQ0uiwcywKyUkUjhl/1OgMQ7GGI7e+yCcNesQGEKAM4aDJ++DtlQ/wMxQ5dnNUx1LQX8+iyEhrGgmiRmNU833jKFBS2JyKuFYMVKaDsZcK4magM6wlBA7RNyeTrLzraj1jTS4oc/2AHkjs7zjZ36t7Vgru635mZv1BBzH6Cjnu9VopDCKOe6q68jqQhDjQzImUUikvoRDCkwEcMaQHEkUUsiV5dYE8i7LKw62OWF4kt85lgUrmslWOgCgKWHWL9Ish91hI++YHzjnHq98pkQPmS9XjqYoIYAdFVUYPWXjBDP5oqrUiCu4q6310nPgTJXjWerdt9t3V76txERBudFD5a6v5xsLQdQqcYpCIsIhBSYCpC9PSVQy/Q95wAxNttFKfAlV20ROiUYyU/W7rrFSusvd7bySTFyfEVXBUT1fPYUUK6Bya0Tt2i9qt2cEQeQkkIxJLSTnHluljHqFopAiIKs6lJZBgVLi8+dQQ7ITnKFBSXk9Y9JktCXMGkaNegond8x1rCudk9owr7UdDOa00IxJzZicNNsO5DLoyww5F/Pc1r2wt1UnSWMMDZruKDUJztGUSCj9E56MuHkhHDlpjUFXOq8pSheHOx0GwMlcGzQO6iwPU/xk1G2DtvMzms55cXH8IwiiOBLxiUKiMOpwyAIzxri2DPehza0Hth1mrJZ6Z4yhQWdIcnNbneuYkp6GwVwWKU2DzjUcM3UmerN7nIRzJ2WHwRlDUyIFKSV2ZfegNdHgTB8BQFpL4PDJ0zGYyyLJNSQ0DUIIZIThRA/lDMOsRWTllzEsvxW1kjVnDI0JDYYQVoQQhxDCySjMGHMqYXPOPWHNdjSR/5htuXCiheDUQnLWwVs2wF9CwBk/+KbhfOeg4JyErCtGJW0Jghh/4qG+EKUgBWaM8H9h1B8AwgqXzttp9GGFGlsOqIP5HHqzGRhSYpKmYyA/jO3DA0hrOvZON6E3O4QhI4fduQxSWgLvDQ+CMaC9oQmGAAaNHAbzebQ3NCFpZeQVUqI/m8VAPgeNMTQnksgJsw9ZkUeSa8haZqUUNyBhFZAEkIL0pOzPCXMKjQHQpXCih5g06zU5wc1qzLJ93Pb4COnJzitN5xV37GShX5BtlZG+98ouQsOXw9qORBkhxYUgiCghJ95wSIGJALUYYTn4g2IMXyZfddpl2MhjR2bY+fzunl5kRN5Zt3VPn/Ng35UbBstlrJ0AvdkMuOLrkrQsKYwx7MoMY8iqEm1IiT15d1ooLwAp3c85oUzhMMuR1/KqzQq377aSUyw0WXXEtatt23i2kQG/kEb5W0i/yAhi4hCXKCRSYcIhH5gI4IxV9YUIy1Vi+LQd4fsc5qClzn1qliLDfNMvQXLCfL5YiUYF9YQiCmGuRExUVpP6/doTxMQlxRlFIdUJpMBEgJByhE5h0vPHs8apphzgGxLQLli6u87w1UTy61uqnHJqDxXbr39xUFj1SKjkflPJLsLaklWGIOqPjJAFPwRrFTsTb7WveoUUmAgImz6y/UFUvNMlpu+HOStj1u7ZkxeOPwxnDCnuzvS1JidhkmZGHaU1HZ2TWpG21qeYjklayknypjMdunWKc0KiN5OFsPbZoCeRsPxhOBh0xpyLQedASol8UvsrJJAxDKsMgunjol5E/i+LJ9JI+euPSHIy7xYZyErvN0HjHkYdf8cJgvBBUUj1AfnAjDFWFnxwZhYhNCznVA4gB7eQ42BewBA55KUEYxwJcIABgnFMTumYl94LOjcTzLXoDejLZRztoU1OMi0f1medMctJGOjP5k2rDmNIa0k0amaHzD5IJDg3Q6EZA88bEFCSz0lbUTN9fpi1TuMAl+5nSNPJ15kGE9JRWsytrb9K2E9Q4j6o7dTPRdqpsgPl+Nb5v9ZBUUgEQRBEbUIKzBihWhAkXOXFXCehM4ZG3bWWaIxD4zryQiArBNJp07IylM+DMYaEplkRQAJgHK3JBuSFgQRnmKSbp3XYMKBzjgTnMIREVgikNDPrbtYww55tOVlhhjcb0pwSYwxgnIHb0UWcgTMOYVmJnBBp21JkhUgLaYZEM+aWaFStLbaSY9d/siOP/BYW6bQtHgbtp1x/FvJ7IYiJDX3P6wNSYCIgaUUhBSWzCwzRtXKq2NgFDm0rRFJz6/dozFVIAKBBeS8BZJTwpQZNQ1Jz0/6nNc1ZxxnQoGuO3ITiBKNWZ7blupl2Ad2ngNjKinp89nuNF9YecvxglOWqtcbb1l2v1kSyFZoggpQaukERBBEEQ3yikCgGKRxSYCLATM0/glpI9vZFZKp//cuL9aPYZ7+8gnV+RcL3vtj2nn0W6bO6viCKydkmqH1wfwJFl1hPEAQBAAkWfh+tJaJwwo3JoY4IcuKNACFHrrwAZrSQM61iKRJBf9UXUPjQNv1MKpcTiHLV2xaZMDlB+ww+VkVmwOegtqWo1GGXIIiJS1YWpqMg4gkpMBGQK8OjXTUFmhYbr2+48/CH6buSF9LJcjuYM5wonWEhkRGusjBJ557Ed7ZviQAwlDfMCCkrUd5wXjjTXDkhnXUMDAmu1DBigDv5VCg7rxyvX3kwZOmEfmGKil+xKZc6/pFBEETExCUKiQiHppDGCGaFSTtKguUg6zqrMgxZUT+AndLfrXI9mBceh9YEs2soMaQ1DgFTG3XkWLLzVqVsezthmPWM7P3oEgAznXIT3NsfrvjGCMtB18Z2vfGn8Lffq9E8Qe/LJSgqiJQVgiAmAlGEQVMYNRFKOQ9m6bNMGGodICnBYEX2WBFACStayBACWQEwa5rJViTMOkUSnEnPQ54xQFcjgpg7rcSZWTmaMYa8lczJMMWAFfjAqGFT0gn9tssB2EqOsI6LgcENslY2LfK+GMXGMsyJlyAIohLoXlIf0BRSBCQ4C5xyUfFGIaEgYskNOWZIcjf/rhpqbKM60BdMxyhWE7WdaWFxc7PY/jJF+1tE22DMrTLtLHP+1nvaJIIg4g5HfKKQiHBqWoFZvnw5PvCBD6C5uRnTpk3Deeedh40bN3raDA8PY8mSJZg6dSqampqwaNEi9PT0jGk/GWMeP5SoZAJB5r/yA+uYT9HwS4kGb3+YbQYaoSSCIIjRRGfhP95qCSolEE5NKzBr1qzBkiVL8PTTT2PVqlXI5XI4/fTTMTg46LS58sor8dBDD+H+++/HmjVrsHXrVpx//vlj2k9DiUIqiLKRI0yDL1VJIW19CfLCrSp2tFDwPlXCLvqwCKaRHG851PF3kCCIMSQrC4vkEvGkpn1gVq5c6fl89913Y9q0adiwYQNOPvlk9PX14c4778R9992HU089FQBw11134eCDD8bTTz+N448/fkz6mfd5tJf6ajBmOs8aVlvN8lGxp5WE5a8CK5mcBjdrr2458grLF0Vn5nSUkKYcBlMuAM80FKw23LHsuOuY77NtXQ1yoHW2YazocY5U41f7EhSpREoMQRBRkBMSWtRmc2LMqWkFxk9fXx8AYMqUKQCADRs2IJfLYcGCBU6befPmYdasWVi7dm1RBSaTySCTyTif+/v7R63P7oPYVRyY9ZRmqmMvYx5zmGp/YbbzrbJeTS7H7QJL9rICh1xvThe7nRvW7ZVb2PfiFM1ZV8a2oXKr3J4gCCLulO8wEC6jXqnpKSQVIQSuuOIKnHDCCTjssMMAAN3d3Ugmk2hra/O0bW9vR3d3d1FZy5cvR2trq/OaOXNmVX0L8lKxsStN2xei/4HPLUWCwbSgaJYiw6119nvNCpl25VhWG+kWgLSnngrluPv39yfMl83/5SlQoEI+EwRB1Cp0r6oPYqPALFmyBC+99BJ+9atfVS3rmmuuQV9fn/PasmVLVfISnDmmLKb8LbSYeLdz0/EHKEFKWn5/xE+xL1/QcjVjv9/S4u9rmNzQ4wr5TBAEUUtoiE8UEovoVa/EYgrp8ssvx8MPP4wnnngCM2bMcJZ3dHQgm82it7fXY4Xp6elBR0dHUXmpVAqpVCqy/pkhyzJ8ziX0KvJqMKEe8hU4mITLKVsMQRBE3aCxEvdGIjbUtAVGSonLL78cDzzwAB599FHMmTPHs/7YY49FIpHA6tWrnWUbN27E5s2b0dXVNWb9VKOQxpMa6AJBEERNk5WFgRe1CoVRh1PTFpglS5bgvvvuw3//93+jubnZ8WtpbW1FQ0MDWltbcemll2Lp0qWYMmUKWlpa8MUvfhFdXV1jFoEEjN+XQXXYpSgdgiCI8shLCZ3umLGnphWY2267DQDw4Q9/2LP8rrvuwmc+8xkAwM033wzOORYtWoRMJoOFCxfiJz/5yRj3dPxwE94RBEEQ9QXFIYVR0wpMsWRpKul0GrfeeituvfXWMehRMBxu7hWCIAiitqlp3wkf9at+VE+czmPNktA4EnSVEQRB1Dw6MyNHifhDCkxE0NeBIAii9rHzaBHxp6ankOJCXkjk4+HUThAEMaHJSTMxaoLX/u938oAJp/bPYAygwmAEQRDxwaBbdl1AFhiCIAiCqEGiyONSz7NlZIGJAPIHIwiCiA/aeHeAiARSYCIgwSkKiSAIIg4kmBk5SsQfmkIiCIIgiBqEnHjDITU0AnJCIEdOYQRBEDVPTgJZQ4x3N4gIIAtMBMSkLhhBEAQBIC7qC2Os6pw19ZzzhiwwBEEQBEHEDlJgIkCvYw2XIAii3qB7dn1AU0gRoHEGLs3kSJwBnDEIKSEkoFnfE0OazlScARLmtBNn5jIBQPrbMkAbIzn251JyONx92uuENJdpI5TjtC0ihzMGQ8pI5NTSOQkaSzondE7onIzNOYnLtAoDOfGGQQpMRDDGoCtXCmfMkx9GXad++QArJ0GRtmMhx/85TE7BPsdCDmORyKm1c+JvS+eEzklVcuicVCSHiD80hUQQBEEQROwgCwxBEARB1CA0hRQOWWAIgiAIgogdZIEhCIIgiBqEijmGQxYYgiAIgiBiBykwBEEQBEHEDppCIgiCIIgahJx4wyEFhiAIgiBqElJhwqApJIIgCIKoQWwn3mpflXLrrbdi3333RTqdxvz58/HMM89Ef3ARQAoMQRAEQRAAgF//+tdYunQpvva1r+HPf/4zjjzySCxcuBDbt28f764VQAoMQRAEQRAAgO9///v43Oc+h4svvhiHHHIIbr/9dkyaNAk///nPx7trBZACQxAEQRA1CIvoVS7ZbBYbNmzAggULnGWccyxYsABr166t+niihpx4AUgpAQD9/f3j3BOCIAiilrGfE/ZzYyz2FYUMv6xUKoVUKuVZ9v7778MwDLS3t3uWt7e3469//WvVfYkaUmAA7N69GwAwc+bMce4JQRAEEQd2796N1tbWUZGdTCbR0dGBufvOjkReU1NTwfPta1/7Gr7+9a9HIn+8IAUGQGdnJ7Zs2YLm5mawCPMu9/f3Y+bMmdiyZQtaWloik0uY0PiOLjS+owuN7+gyWuMrpcTu3bvR2dkZmUw/6XQamzZtQjabjUSelLLg2ea3vgDAXnvtBU3T0NPT41ne09ODjo6OSPoSJaTAwJzjmzFjxqjJb2lpoRvUKELjO7rQ+I4uNL6jy2iM72hZXlTS6TTS6fSo70clmUzi2GOPxerVq3HeeecBAIQQWL16NS6//PIx7Us5kAJDEARBEAQAYOnSpbjoootw3HHH4YMf/CB+8IMfYHBwEBdffPF4d60AUmAIgiAIggAAfOITn8B7772HZcuWobu7G0cddRRWrlxZ4NhbC5ACM4qkUil87WtfC5xrJKqHxnd0ofEdXWh8Rxca35Fz+eWX1+SUkR8mxyIWjCAIgiAIIkIokR1BEARBELGDFBiCIAiCIGIHKTAEQRAEQcQOUmCqZPny5fjABz6A5uZmTJs2Deeddx42btzoaTM8PIwlS5Zg6tSpaGpqwqJFiwoSBRHlceONN4IxhiuuuMJZRuNbHe+++y4+9alPYerUqWhoaMDhhx+OZ5991lkvpcSyZcswffp0NDQ0YMGCBXj99dfHscfxwTAMXHfddZgzZw4aGhqw//7745vf/KYnDT2Nb/k88cQTOOecc9DZ2QnGGB588EHP+nLGcufOnVi8eDFaWlrQ1taGSy+9FAMDA2N4FERUkAJTJWvWrMGSJUvw9NNPY9WqVcjlcjj99NMxODjotLnyyivx0EMP4f7778eaNWuwdetWnH/++ePY63iyfv16/PSnP8URRxzhWU7jO3J27dqFE044AYlEAr///e/xyiuv4Hvf+x4mT57stFmxYgVuueUW3H777Vi3bh0aGxuxcOFCDA8Pj2PP48FNN92E2267DT/+8Y/x6quv4qabbsKKFSvwox/9yGlD41s+g4ODOPLII3HrrbcGri9nLBcvXoyXX34Zq1atwsMPP4wnnngCl1122VgdAhElkoiU7du3SwByzZo1Ukope3t7ZSKRkPfff7/T5tVXX5UA5Nq1a8erm7Fj9+7dcu7cuXLVqlXy7/7u7+SXvvQlKSWNb7VcffXV8sQTTyy6XgghOzo65He+8x1nWW9vr0ylUvI//uM/xqKLseass86Sl1xyiWfZ+eefLxcvXiylpPGtBgDygQcecD6XM5avvPKKBCDXr1/vtPn9738vGWPy3XffHbO+E9FAFpiI6evrAwBMmTIFALBhwwbkcjlPefJ58+Zh1qxZNVmevFZZsmQJzjrrLM84AjS+1fI///M/OO644/Cxj30M06ZNw9FHH42f/exnzvpNmzahu7vbM76tra2YP38+jW8ZfOhDH8Lq1avx2muvAQBeeOEFPPnkkzjzzDMB0PhGSTljuXbtWrS1teG4445z2ixYsACcc6xbt27M+0xUByWyixAhBK644gqccMIJOOywwwAA3d3dSCaTaGtr87Rtb29Hd3f3OPQyfvzqV7/Cn//8Z6xfv75gHY1vdbz11lu47bbbsHTpUvx//9//h/Xr1+Of//mfkUwmcdFFFzlj6M/CSeNbHl/96lfR39+PefPmQdM0GIaBG264AYsXLwYAGt8IKWcsu7u7MW3aNM96XdcxZcoUGu8YQgpMhCxZsgQvvfQSnnzyyfHuSt2wZcsWfOlLX8KqVavGvLDZREAIgeOOOw7f/va3AQBHH300XnrpJdx+++246KKLxrl38ec3v/kN7r33Xtx333049NBD8fzzz+OKK65AZ2cnjS9BVAlNIUXE5ZdfjocffhiPPfaYp7J1R0cHstksent7Pe1rtTx5rbFhwwZs374dxxxzDHRdh67rWLNmDW655Rbouo729nYa3yqYPn06DjnkEM+ygw8+GJs3bwYAZwz9UV00vuXxla98BV/96ldxwQUX4PDDD8enP/1pXHnllVi+fDkAGt8oKWcsOzo6sH37ds/6fD6PnTt30njHEFJgqkRKicsvvxwPPPAAHn30UcyZM8ez/thjj0UikcDq1audZRs3bsTmzZvR1dU11t2NHaeddhpefPFFPP/8887ruOOOw+LFi533NL4j54QTTigI+3/ttdcwe/ZsAMCcOXPQ0dHhGd/+/n6sW7eOxrcM9uzZA869t1lN0yCEAEDjGyXljGVXVxd6e3uxYcMGp82jjz4KIQTmz58/5n0mqmS8vYjjzhe+8AXZ2toqH3/8cblt2zbntWfPHqfN5z//eTlr1iz56KOPymeffVZ2dXXJrq6ucex1vFGjkKSk8a2GZ555Ruq6Lm+44Qb5+uuvy3vvvVdOmjRJ/vKXv3Ta3HjjjbKtrU3+93//t/zLX/4izz33XDlnzhw5NDQ0jj2PBxdddJHcZ5995MMPPyw3bdokf/vb38q99tpLXnXVVU4bGt/y2b17t3zuuefkc889JwHI73//+/K5556Tb7/9tpSyvLE844wz5NFHHy3XrVsnn3zySTl37lx54YUXjtchEVVACkyVAAh83XXXXU6boaEh+U//9E9y8uTJctKkSfKjH/2o3LZt2/h1Oub4FRga3+p46KGH5GGHHSZTqZScN2+evOOOOzzrhRDyuuuuk+3t7TKVSsnTTjtNbty4cZx6Gy/6+/vll770JTlr1iyZTqflfvvtJ//1X/9VZjIZpw2Nb/k89thjgffbiy66SEpZ3lju2LFDXnjhhbKpqUm2tLTIiy++WO7evXscjoaoFqpGTRAEQRBE7CAfGIIgCIIgYgcpMARBEARBxA5SYAiCIAiCiB2kwBAEQRAEETtIgSEIgiAIInaQAkMQBEEQROwgBYYgCIIgiNhBCgxBEARBELGDFBiCIAiCIGIHKTAEQVTFZz7zGZx33nkFyx9//HEwxgoqhRMEQUQBKTAEQRAEQcQOUmAIoo5ZuXIlTjzxRLS1tWHq1Kk4++yz8eabbzrrn3rqKRx11FFIp9M47rjj8OCDD4Ixhueff95p89JLL+HMM89EU1MT2tvb8elPfxrvv//+OBwNQRCECykwBFHHDA4OYunSpXj22WexevVqcM7x0Y9+FEII9Pf345xzzsHhhx+OP//5z/jmN7+Jq6++2rN9b28vTj31VBx99NF49tlnsXLlSvT09ODjH//4OB0RQRCEiT7eHSAIYvRYtGiR5/PPf/5z7L333njllVfw5JNPgjGGn/3sZ0in0zjkkEPw7rvv4nOf+5zT/sc//jGOPvpofPvb3/bImDlzJl577TUceOCBAICHH34YTU1Nnn0ZhjGKR0YQxESHFBiCqGNef/11LFu2DOvWrcP7778PIQQAYPPmzdi4cSOOOOIIpNNpp/0HP/hBz/YvvPACHnvssQLlBADefPNNR4E55ZRTcNttt3nWr1u3Dp/61KeiPiSCIAgApMAQRF1zzjnnYPbs2fjZz36Gzs5OCCFw2GGHIZvNlrX9wMAAzjnnHNx0000F66ZPn+68b2xsxAEHHOBZ/84771TXeYIgiBBIgSGIOmXHjh3YuHEjfvazn+Gkk04CADz55JPO+oMOOgi//OUvkclkkEqlAADr16/3yDjmmGPwX//1X9h3332h63S7IAiidiAnXoKoUyZPnoypU6fijjvuwBtvvIFHH30US5cuddZ/8pOfhBACl112GV599VU88sgj+O53vwsAYIwBAJYsWYKdO3fiwgsvxPr16/Hmm2/ikUcewcUXX0w+LgRBjCukwBBEncI5x69+9Sts2LABhx12GK688kp85zvfcda3tLTgoYcewvPPP4+jjjoK//qv/4ply5YBgOMX09nZiT/96U8wDAOnn346Dj/8cFxxxRVoa2sD53T7IAhi/GBSSjnenSAIoja49957cfHFF6Ovrw8NDQ3j3R2CIIii0KQ2QUxgfvGLX2C//fbDPvvsgxdeeAFXX301Pv7xj5PyQhBEzUMKDEFMYLq7u7Fs2TJ0d3dj+vTp+NjHPoYbbrhhvLtFEARREppCIgiCIAgidpAXHkEQBEEQsYMUGIIgCIIgYgcpMARBEARBxA5SYAiCIAiCiB2kwBAEQRAEETtIgSEIgiAIInaQAkMQBEEQROwgBYYgCIIgiNhBCgxBEARBELHj/weWcegv3bK8ogAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -6095,12 +6090,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -6129,12 +6124,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -6154,20 +6149,25 @@
     "repsem[\"nb\"].plot(ax=ax, secondary_y=True)\n",
     "ax.set_title(\"distribution des mariages par jour de la semaine\");"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/conf.py b/_doc/conf.py
index ba8e12f..189e660 100644
--- a/_doc/conf.py
+++ b/_doc/conf.py
@@ -4,7 +4,6 @@
 from sphinx_runpython.github_link import make_linkcode_resolve
 from teachpyx import __version__
 
-
 extensions = [
     "nbsphinx",
     "sphinx.ext.autodoc",
@@ -72,7 +71,7 @@
 """
 
 # The following is used by sphinx.ext.linkcode to provide links to github
-linkcode_resolve = make_linkcode_resolve(
+_linkcode_resolve = make_linkcode_resolve(
     "teachpyx",
     (
         "https://github.com/sdpython/teachpyx/"
@@ -81,6 +80,11 @@
     ),
 )
 
+
+def linkcode_resolve(domain, info):
+    return _linkcode_resolve(domain, info)
+
+
 latex_elements = {
     "papersize": "a4",
     "pointsize": "10pt",
diff --git a/_doc/examples/prog/plot_einstein_riddle.py b/_doc/examples/prog/plot_einstein_riddle.py
index da1de5f..5c4fd03 100644
--- a/_doc/examples/prog/plot_einstein_riddle.py
+++ b/_doc/examples/prog/plot_einstein_riddle.py
@@ -87,11 +87,11 @@
 On commence par la fonction `permutation`:
 qui énumère les permutations d'un ensemble :
 """
+
 import copy
 from io import StringIO
 import pandas
 
-
 ##########################
 # Fonction permutation
 # ====================
diff --git a/_doc/examples/prog/plot_float_and_double_rouding.py b/_doc/examples/prog/plot_float_and_double_rouding.py
index d6d09f1..f184755 100644
--- a/_doc/examples/prog/plot_float_and_double_rouding.py
+++ b/_doc/examples/prog/plot_float_and_double_rouding.py
@@ -20,7 +20,6 @@
 import pandas
 import matplotlib.pyplot as plt
 
-
 rnd = numpy.random.random(100000000)
 rnd.shape, rnd.dtype
 
diff --git a/_doc/examples/prog/plot_gil_example.py b/_doc/examples/prog/plot_gil_example.py
index 4e64c81..bd7ce23 100644
--- a/_doc/examples/prog/plot_gil_example.py
+++ b/_doc/examples/prog/plot_gil_example.py
@@ -17,6 +17,7 @@
 On mesure le temps nécessaire pour créer deux liste et comparer ce
 temps avec celui que cela prendrait en parallèle.
 """
+
 import timeit
 import time
 from concurrent.futures import ThreadPoolExecutor
diff --git a/_doc/examples/prog/plot_numpy_tricks.py b/_doc/examples/prog/plot_numpy_tricks.py
index 3c73243..92d0b67 100644
--- a/_doc/examples/prog/plot_numpy_tricks.py
+++ b/_doc/examples/prog/plot_numpy_tricks.py
@@ -9,6 +9,7 @@
 accéder à un élément en particulier
 ===================================
 """
+
 import timeit
 
 import numpy
diff --git a/_doc/examples/prog/plot_pandas_groupby.py b/_doc/examples/prog/plot_pandas_groupby.py
index 07cf758..238636a 100644
--- a/_doc/examples/prog/plot_pandas_groupby.py
+++ b/_doc/examples/prog/plot_pandas_groupby.py
@@ -14,10 +14,8 @@
 ============================
 """
 
-
 import pandas
 
-
 data = [{"a": 1, "b": 2}, {"a": 10, "b": 20}, {"b": 3}, {"b": 4}]
 df = pandas.DataFrame(data)
 df
diff --git a/_doc/examples/prog/plot_serialisation_examples.py b/_doc/examples/prog/plot_serialisation_examples.py
index 49fc35f..d943aac 100644
--- a/_doc/examples/prog/plot_serialisation_examples.py
+++ b/_doc/examples/prog/plot_serialisation_examples.py
@@ -17,6 +17,7 @@
 Ecriture (json)
 +++++++++++++++
 """
+
 from io import StringIO, BytesIO
 import timeit
 import json
@@ -25,7 +26,6 @@
 import cloudpickle
 import pickle
 
-
 data = {
     "records": [
         {
diff --git a/_doc/examples/prog/plot_serialisation_protobuf.py b/_doc/examples/prog/plot_serialisation_protobuf.py
index cd71f77..1ff3b30 100644
--- a/_doc/examples/prog/plot_serialisation_protobuf.py
+++ b/_doc/examples/prog/plot_serialisation_protobuf.py
@@ -25,6 +25,7 @@
 On récupère l'exemple du `tutorial
 <https://developers.google.com/protocol-buffers/docs/pythontutorial>`_.
 """
+
 import os
 import sys
 import timeit
diff --git a/_doc/examples/prog/plot_tarabiscote.py b/_doc/examples/prog/plot_tarabiscote.py
index a68fbab..5fc3ac4 100644
--- a/_doc/examples/prog/plot_tarabiscote.py
+++ b/_doc/examples/prog/plot_tarabiscote.py
@@ -16,6 +16,7 @@
 listes contenues dans ``ens``. Le résultat retourné est effectivement
 celui désiré mais la fonction modifie également la liste ``ens``, pourquoi ?
 """
+
 import math
 import copy
 import numpy
diff --git a/_doc/practice/ml/winesc_multi.ipynb b/_doc/practice/ml/winesc_multi.ipynb
index 7975365..e6a7a08 100644
--- a/_doc/practice/ml/winesc_multi.ipynb
+++ b/_doc/practice/ml/winesc_multi.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,15 +46,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -66,31 +82,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-5.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-5.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -102,7 +119,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -110,7 +127,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-5 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -120,7 +137,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -136,14 +153,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -151,28 +168,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -190,24 +207,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -215,27 +241,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -243,79 +267,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -325,18 +348,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -352,23 +375,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -378,7 +403,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -388,7 +414,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -423,12 +450,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -438,31 +465,514 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-5');</script></body>"
       ],
       "text/plain": [
-       "LogisticRegression(solver='liblinear')"
+       "LogisticRegression()"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,22 +980,30 @@
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
-    "clr = LogisticRegression(solver=\"liblinear\")\n",
+    "clr = LogisticRegression()\n",
     "clr.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/2645516986.py:3: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "55.07692307692308"
+       "np.float64(46.52307692307692)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -505,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +1053,6 @@
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
-       "      <th>6</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -543,9 +1060,8 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -553,60 +1069,45 @@
        "      <th>1</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>47</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>332</td>\n",
-       "      <td>184</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>316</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>169</td>\n",
-       "      <td>541</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
+       "      <td>177</td>\n",
+       "      <td>511</td>\n",
+       "      <td>7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>217</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>42</td>\n",
+       "      <td>35</td>\n",
+       "      <td>212</td>\n",
        "      <td>5</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>44</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -614,17 +1115,16 @@
        "</div>"
       ],
       "text/plain": [
-       "   0  1    2    3   4  5  6\n",
-       "0  0  0    4    7   0  0  0\n",
-       "1  0  0   47   22   0  0  0\n",
-       "2  0  0  332  184   0  0  0\n",
-       "3  0  0  169  541  10  0  0\n",
-       "4  0  0   19  217  22  0  0\n",
-       "5  0  0    3   42   5  0  0\n",
-       "6  0  0    0    1   0  0  0"
+       "   0  1    2    3  4  5\n",
+       "0  0  0    5    5  0  0\n",
+       "1  0  0   15   39  1  0\n",
+       "2  0  0  240  316  5  0\n",
+       "3  0  0  177  511  7  0\n",
+       "4  0  0   35  212  5  0\n",
+       "5  0  0    6   44  2  0"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -645,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -675,7 +1175,6 @@
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
-       "      <th>9</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -683,9 +1182,8 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -693,60 +1191,45 @@
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>47</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>332</td>\n",
-       "      <td>184</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>316</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>169</td>\n",
-       "      <td>541</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
+       "      <td>177</td>\n",
+       "      <td>511</td>\n",
+       "      <td>7</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>217</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>42</td>\n",
+       "      <td>35</td>\n",
+       "      <td>212</td>\n",
        "      <td>5</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <th>8</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>44</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -754,17 +1237,16 @@
        "</div>"
       ],
       "text/plain": [
-       "   3  4    5    6   7  8  9\n",
-       "3  0  0    4    7   0  0  0\n",
-       "4  0  0   47   22   0  0  0\n",
-       "5  0  0  332  184   0  0  0\n",
-       "6  0  0  169  541  10  0  0\n",
-       "7  0  0   19  217  22  0  0\n",
-       "8  0  0    3   42   5  0  0\n",
-       "9  0  0    0    1   0  0  0"
+       "   3  4    5    6  7  8\n",
+       "3  0  0    5    5  0  0\n",
+       "4  0  0   15   39  1  0\n",
+       "5  0  0  240  316  5  0\n",
+       "6  0  0  177  511  7  0\n",
+       "7  0  0   35  212  5  0\n",
+       "8  0  0    6   44  2  0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -793,15 +1275,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -813,31 +1296,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-6.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-6.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-6 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-6 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -849,7 +1333,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -857,7 +1341,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-6 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -867,7 +1351,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -883,14 +1367,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -898,28 +1382,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-6 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -937,24 +1421,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -962,27 +1455,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -990,79 +1481,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1072,18 +1562,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1099,23 +1589,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1125,7 +1617,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1135,7 +1628,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1170,12 +1664,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1185,35 +1679,576 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "OneVsRestClassifier(estimator=LogisticRegression(solver='liblinear'))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegre...r=&#x27;liblinear&#x27;)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;liblinear&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-6');</script></body>"
+      ],
+      "text/plain": [
+       "OneVsRestClassifier(estimator=LogisticRegression(solver='liblinear'))"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.multiclass import OneVsRestClassifier\n",
     "\n",
@@ -1223,16 +2258,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "54.95384615384615"
+       "np.float64(52.184615384615384)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1250,15 +2293,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {\n",
+       "<style>#sk-container-id-7 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1270,31 +2314,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-7.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-7.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 {\n",
+       "#sk-container-id-7 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 pre {\n",
+       "#sk-container-id-7 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1306,7 +2351,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1314,7 +2359,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-container {\n",
+       "#sk-container-id-7 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1324,7 +2369,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1340,14 +2385,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1355,28 +2400,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
+       "#sk-container-id-7 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1394,24 +2439,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1419,27 +2473,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1447,79 +2499,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1529,18 +2580,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1556,23 +2607,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1582,7 +2635,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1592,7 +2646,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1627,12 +2682,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1642,31 +2697,556 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsOneClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsOneClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=LogisticRegression(solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsOneClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegre...r=&#x27;liblinear&#x27;)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes * (<br>n_classes - 1) / 2` OVO problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;liblinear&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
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+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
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+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
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       ],
       "text/plain": [
        "OneVsOneClassifier(estimator=LogisticRegression(solver='liblinear'))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1680,16 +3260,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "55.138461538461534"
+       "np.float64(52.12307692307693)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1700,7 +3288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1730,7 +3318,6 @@
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
-       "      <th>9</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1738,19 +3325,17 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>5</td>\n",
+       "      <td>4</td>\n",
        "      <td>6</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>46</td>\n",
-       "      <td>23</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -1758,20 +3343,18 @@
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>332</td>\n",
-       "      <td>183</td>\n",
+       "      <td>329</td>\n",
+       "      <td>229</td>\n",
+       "      <td>2</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>169</td>\n",
-       "      <td>524</td>\n",
-       "      <td>27</td>\n",
-       "      <td>0</td>\n",
+       "      <td>172</td>\n",
+       "      <td>485</td>\n",
+       "      <td>38</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1779,29 +3362,17 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>18</td>\n",
-       "      <td>200</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
+       "      <td>202</td>\n",
        "      <td>32</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <th>8</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>30</td>\n",
+       "      <td>17</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1809,17 +3380,16 @@
        "</div>"
       ],
       "text/plain": [
-       "   3  4    5    6   7  8  9\n",
-       "3  0  0    5    6   0  0  0\n",
-       "4  0  0   46   23   0  0  0\n",
-       "5  0  0  332  183   1  0  0\n",
-       "6  0  0  169  524  27  0  0\n",
-       "7  0  0   18  200  40  0  0\n",
-       "8  0  0    6   32  12  0  0\n",
-       "9  0  0    0    1   0  0  0"
+       "   3  4    5    6   7  8\n",
+       "3  0  0    4    6   0  0\n",
+       "4  0  1   27   27   0  0\n",
+       "5  0  0  329  229   2  1\n",
+       "6  0  0  172  485  38  0\n",
+       "7  0  0   18  202  32  0\n",
+       "8  0  0    5   30  17  0"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1848,15 +3418,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {\n",
+       "<style>#sk-container-id-8 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1868,31 +3439,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-8.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-8.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 {\n",
+       "#sk-container-id-8 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 pre {\n",
+       "#sk-container-id-8 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1904,7 +3476,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1912,7 +3484,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-container {\n",
+       "#sk-container-id-8 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1922,7 +3494,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1938,14 +3510,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1953,28 +3525,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
+       "#sk-container-id-8 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1992,24 +3564,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2017,27 +3598,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2045,79 +3624,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2127,18 +3705,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2154,23 +3732,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -2180,7 +3760,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2190,7 +3771,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2225,12 +3807,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -2240,31 +3822,498 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-8');</script></body>"
       ],
       "text/plain": [
        "DecisionTreeClassifier()"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2278,16 +4327,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "59.323076923076925"
+       "np.float64(59.50769230769231)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2305,15 +4362,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-6 {\n",
+       "<style>#sk-container-id-9 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -2325,31 +4383,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-9.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-9.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 {\n",
+       "#sk-container-id-9 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 pre {\n",
+       "#sk-container-id-9 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2361,7 +4420,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2369,7 +4428,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-container {\n",
+       "#sk-container-id-9 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2379,7 +4438,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2395,14 +4454,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2410,28 +4469,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
+       "#sk-container-id-9 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2449,24 +4508,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2474,27 +4542,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2502,79 +4568,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
+       "#sk-container-id-9 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2584,18 +4649,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2611,23 +4676,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -2637,7 +4704,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2647,7 +4715,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2682,12 +4751,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -2697,31 +4766,556 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=DecisionTreeClassifier())</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">DecisionTreeClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-9');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=DecisionTreeClassifier())"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2733,16 +5327,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "53.35384615384615"
+       "np.float64(56.61538461538461)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2760,15 +5362,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-7 {\n",
+       "<style>#sk-container-id-10 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -2780,31 +5383,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-10.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-10.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 {\n",
+       "#sk-container-id-10 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 pre {\n",
+       "#sk-container-id-10 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "#sk-container-id-10 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2816,7 +5420,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2824,7 +5428,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-container {\n",
+       "#sk-container-id-10 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2834,7 +5438,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2850,14 +5454,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
+       "#sk-container-id-10 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2865,28 +5469,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "#sk-container-id-10 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
+       "#sk-container-id-10 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2904,24 +5508,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
+       "#sk-container-id-10 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "#sk-container-id-10 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2929,27 +5542,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-10 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2957,79 +5568,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-10 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-10 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
+       "#sk-container-id-10 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
+       "#sk-container-id-10 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3039,18 +5649,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "#sk-container-id-10 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3066,23 +5676,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -3092,7 +5704,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3102,7 +5715,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3137,12 +5751,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "#sk-container-id-10 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -3152,31 +5766,540 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsOneClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsOneClassifier(estimator=DecisionTreeClassifier())</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsOneClassifier(estimator=DecisionTreeClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsOneClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html\">?<span>Documentation for OneVsOneClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">DecisionTreeClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes * (<br>n_classes - 1) / 2` OVO problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at<br>  each split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. note::<br><br>    The search for a split does not stop until at least one<br>    valid partition of the node samples is found, even if it requires to<br>    effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=class_weight,-dict%2C%20list%20of%20dict%20or%20%22balanced%22%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict, list of dict or \"balanced\", default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If None, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-10');</script></body>"
       ],
       "text/plain": [
        "OneVsOneClassifier(estimator=DecisionTreeClassifier())"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3188,16 +6311,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "62.58461538461538"
+       "np.float64(59.815384615384616)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3215,15 +6346,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-8 {\n",
+       "<style>#sk-container-id-11 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -3235,31 +6367,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-11.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-11.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 {\n",
+       "#sk-container-id-11 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 pre {\n",
+       "#sk-container-id-11 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "#sk-container-id-11 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3271,7 +6404,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3279,7 +6412,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-container {\n",
+       "#sk-container-id-11 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3289,7 +6422,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3305,14 +6438,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel {\n",
+       "#sk-container-id-11 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3320,28 +6453,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "#sk-container-id-11 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-8 div.sk-serial {\n",
+       "#sk-container-id-11 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3359,24 +6492,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable {\n",
+       "#sk-container-id-11 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "#sk-container-id-11 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-11 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3384,27 +6526,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-11 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3412,79 +6552,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-8 div.sk-label label {\n",
+       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-11 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-8 div.sk-label label {\n",
+       "#sk-container-id-11 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-label-container {\n",
+       "#sk-container-id-11 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-8 div.sk-estimator {\n",
+       "#sk-container-id-11 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3494,18 +6633,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "#sk-container-id-11 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "#sk-container-id-11 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3521,23 +6660,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -3547,7 +6688,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3557,7 +6699,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -3592,12 +6735,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "#sk-container-id-11 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -3607,31 +6750,594 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-11');</script></body>"
       ],
       "text/plain": [
        "RandomForestClassifier()"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3645,16 +7351,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "69.2923076923077"
+       "np.float64(67.44615384615385)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3665,15 +7379,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-9 {\n",
+       "<style>#sk-container-id-12 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -3685,31 +7400,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-12.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-12.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 {\n",
+       "#sk-container-id-12 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 pre {\n",
+       "#sk-container-id-12 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "#sk-container-id-12 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3721,7 +7437,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3729,7 +7445,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-container {\n",
+       "#sk-container-id-12 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3739,7 +7455,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3755,14 +7471,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "#sk-container-id-12 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel {\n",
+       "#sk-container-id-12 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3770,28 +7486,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "#sk-container-id-12 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-9 div.sk-serial {\n",
+       "#sk-container-id-12 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3809,24 +7525,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable {\n",
+       "#sk-container-id-12 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "#sk-container-id-12 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-12 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3834,27 +7559,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-12 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3862,79 +7585,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-9 div.sk-label label {\n",
+       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-12 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-9 div.sk-label label {\n",
+       "#sk-container-id-12 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-label-container {\n",
+       "#sk-container-id-12 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-9 div.sk-estimator {\n",
+       "#sk-container-id-12 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3944,18 +7666,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "#sk-container-id-12 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "#sk-container-id-12 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3971,23 +7693,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -3997,7 +7721,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4007,7 +7732,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4042,12 +7768,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "#sk-container-id-12 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -4057,31 +7783,652 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
-       "/* On hover */\n",
-       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
+       "/* On hover */\n",
+       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
        "}\n",
        "\n",
-       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=RandomForestClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">RandomForestClassifier()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: RandomForestClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
        "}\n",
-       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=RandomForestClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=RandomForestClassifier())</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       "forceTheme('sk-container-id-12');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=RandomForestClassifier())"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4093,16 +8440,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "69.41538461538461"
+       "np.float64(67.75384615384615)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4120,15 +8475,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-10 {\n",
+       "<style>#sk-container-id-13 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -4140,31 +8496,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-13.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-13.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 {\n",
+       "#sk-container-id-13 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 pre {\n",
+       "#sk-container-id-13 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-hidden--visually {\n",
+       "#sk-container-id-13 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4176,7 +8533,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-13 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4184,7 +8541,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-container {\n",
+       "#sk-container-id-13 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4194,7 +8551,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-13 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4210,14 +8567,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item::after {\n",
+       "#sk-container-id-13 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel {\n",
+       "#sk-container-id-13 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4225,28 +8582,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item {\n",
+       "#sk-container-id-13 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-10 div.sk-serial {\n",
+       "#sk-container-id-13 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4264,24 +8621,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable {\n",
+       "#sk-container-id-13 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-10 label.sk-toggleable__label {\n",
+       "#sk-container-id-13 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-13 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4289,27 +8655,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-13 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-13 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-13 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4317,79 +8681,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-10 div.sk-label label {\n",
+       "#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-13 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-10 div.sk-label label {\n",
+       "#sk-container-id-13 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-label-container {\n",
+       "#sk-container-id-13 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-10 div.sk-estimator {\n",
+       "#sk-container-id-13 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4399,18 +8762,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted {\n",
+       "#sk-container-id-13 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-10 div.sk-estimator:hover {\n",
+       "#sk-container-id-13 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-13 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4426,23 +8789,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -4452,7 +8817,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4462,7 +8828,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4497,12 +8864,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link {\n",
+       "#sk-container-id-13 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -4512,31 +8879,658 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-13 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-13 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=hidden_layer_sizes,-array-like%20of%20shape%28n_layers%20-%202%2C%29%2C%20default%3D%28100%2C%29\">\n",
+       "            hidden_layer_sizes\n",
+       "            <span class=\"param-doc-description\">hidden_layer_sizes: array-like of shape(n_layers - 2,), default=(100,)<br><br>The ith element represents the number of neurons in the ith<br>hidden layer.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('activation',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=activation,-%7B%27identity%27%2C%20%27logistic%27%2C%20%27tanh%27%2C%20%27relu%27%7D%2C%20default%3D%27relu%27\">\n",
+       "            activation\n",
+       "            <span class=\"param-doc-description\">activation: {'identity', 'logistic', 'tanh', 'relu'}, default='relu'<br><br>Activation function for the hidden layer.<br><br>- 'identity', no-op activation, useful to implement linear bottleneck,<br>  returns f(x) = x<br><br>- 'logistic', the logistic sigmoid function,<br>  returns f(x) = 1 / (1 + exp(-x)).<br><br>- 'tanh', the hyperbolic tan function,<br>  returns f(x) = tanh(x).<br><br>- 'relu', the rectified linear unit function,<br>  returns f(x) = max(0, x)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27sgd%27%2C%20%27adam%27%7D%2C%20default%3D%27adam%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'sgd', 'adam'}, default='adam'<br><br>The solver for weight optimization.<br><br>- 'lbfgs' is an optimizer in the family of quasi-Newton methods.<br><br>- 'sgd' refers to stochastic gradient descent.<br><br>- 'adam' refers to a stochastic gradient-based optimizer proposed<br>  by Kingma, Diederik, and Jimmy Ba<br><br>For a comparison between Adam optimizer and SGD, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`.<br><br>Note: The default solver 'adam' works pretty well on relatively<br>large datasets (with thousands of training samples or more) in terms of<br>both training time and validation score.<br>For small datasets, however, 'lbfgs' can converge faster and perform<br>better.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=alpha,-float%2C%20default%3D0.0001\">\n",
+       "            alpha\n",
+       "            <span class=\"param-doc-description\">alpha: float, default=0.0001<br><br>Strength of the L2 regularization term. The L2 regularization term<br>is divided by the sample size when added to the loss.<br><br>For an example usage and visualization of varying regularization, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('batch_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=batch_size,-int%2C%20default%3D%27auto%27\">\n",
+       "            batch_size\n",
+       "            <span class=\"param-doc-description\">batch_size: int, default='auto'<br><br>Size of minibatches for stochastic optimizers.<br>If the solver is 'lbfgs', the classifier will not use minibatch.<br>When set to \"auto\", `batch_size=min(200, n_samples)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate,-%7B%27constant%27%2C%20%27invscaling%27%2C%20%27adaptive%27%7D%2C%20default%3D%27constant%27\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: {'constant', 'invscaling', 'adaptive'}, default='constant'<br><br>Learning rate schedule for weight updates.<br><br>- 'constant' is a constant learning rate given by<br>  'learning_rate_init'.<br><br>- 'invscaling' gradually decreases the learning rate at each<br>  time step 't' using an inverse scaling exponent of 'power_t'.<br>  effective_learning_rate = learning_rate_init / pow(t, power_t)<br><br>- 'adaptive' keeps the learning rate constant to<br>  'learning_rate_init' as long as training loss keeps decreasing.<br>  Each time two consecutive epochs fail to decrease training loss by at<br>  least tol, or fail to increase validation score by at least tol if<br>  'early_stopping' is on, the current learning rate is divided by 5.<br><br>Only used when ``solver='sgd'``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate_init,-float%2C%20default%3D0.001\">\n",
+       "            learning_rate_init\n",
+       "            <span class=\"param-doc-description\">learning_rate_init: float, default=0.001<br><br>The initial learning rate used. It controls the step-size<br>in updating the weights. Only used when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('power_t',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=power_t,-float%2C%20default%3D0.5\">\n",
+       "            power_t\n",
+       "            <span class=\"param-doc-description\">power_t: float, default=0.5<br><br>The exponent for inverse scaling learning rate.<br>It is used in updating effective learning rate when the learning_rate<br>is set to 'invscaling'. Only used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_iter,-int%2C%20default%3D200\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=200<br><br>Maximum number of iterations. The solver iterates until convergence<br>(determined by 'tol') or this number of iterations. For stochastic<br>solvers ('sgd', 'adam'), note that this determines the number of epochs<br>(how many times each data point will be used), not the number of<br>gradient steps.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">600</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shuffle',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=shuffle,-bool%2C%20default%3DTrue\">\n",
+       "            shuffle\n",
+       "            <span class=\"param-doc-description\">shuffle: bool, default=True<br><br>Whether to shuffle samples in each iteration. Only used when<br>solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Determines random number generation for weights and bias<br>initialization, train-test split if early stopping is used, and batch<br>sampling when solver='sgd' or 'adam'.<br>Pass an int for reproducible results across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the optimization. When the loss or score is not improving<br>by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,<br>unless ``learning_rate`` is set to 'adaptive', convergence is<br>considered to be reached and training stops.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Whether to print progress messages to stdout.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous<br>call to fit as initialization, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=momentum,-float%2C%20default%3D0.9\">\n",
+       "            momentum\n",
+       "            <span class=\"param-doc-description\">momentum: float, default=0.9<br><br>Momentum for gradient descent update. Should be between 0 and 1. Only<br>used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=nesterovs_momentum,-bool%2C%20default%3DTrue\">\n",
+       "            nesterovs_momentum\n",
+       "            <span class=\"param-doc-description\">nesterovs_momentum: bool, default=True<br><br>Whether to use Nesterov's momentum. Only used when solver='sgd' and<br>momentum > 0.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=early_stopping,-bool%2C%20default%3DFalse\">\n",
+       "            early_stopping\n",
+       "            <span class=\"param-doc-description\">early_stopping: bool, default=False<br><br>Whether to use early stopping to terminate training when validation<br>score is not improving. If set to True, it will automatically set<br>aside ``validation_fraction`` of training data as validation and<br>terminate training when validation score is not improving by at least<br>``tol`` for ``n_iter_no_change`` consecutive epochs. The split is<br>stratified, except in a multilabel setting.<br>If early stopping is False, then the training stops when the training<br>loss does not improve by more than ``tol`` for ``n_iter_no_change``<br>consecutive passes over the training set.<br>Only effective when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Must be between 0 and 1.<br>Only used if early_stopping is True.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_1',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_1,-float%2C%20default%3D0.9\">\n",
+       "            beta_1\n",
+       "            <span class=\"param-doc-description\">beta_1: float, default=0.9<br><br>Exponential decay rate for estimates of first moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_2',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_2,-float%2C%20default%3D0.999\">\n",
+       "            beta_2\n",
+       "            <span class=\"param-doc-description\">beta_2: float, default=0.999<br><br>Exponential decay rate for estimates of second moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.999</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=epsilon,-float%2C%20default%3D1e-8\">\n",
+       "            epsilon\n",
+       "            <span class=\"param-doc-description\">epsilon: float, default=1e-8<br><br>Value for numerical stability in adam. Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-08</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3D10\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=10<br><br>Maximum number of epochs to not meet ``tol`` improvement.<br>Only effective when solver='sgd' or 'adam'.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_fun',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_fun,-int%2C%20default%3D15000\">\n",
+       "            max_fun\n",
+       "            <span class=\"param-doc-description\">max_fun: int, default=15000<br><br>Only used when solver='lbfgs'. Maximum number of loss function calls.<br>The solver iterates until convergence (determined by 'tol'), number<br>of iterations reaches max_iter, or this number of loss function calls.<br>Note that number of loss function calls will be greater than or equal<br>to the number of iterations for the `MLPClassifier`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">15000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-13');</script></body>"
       ],
       "text/plain": [
        "MLPClassifier(hidden_layer_sizes=30, max_iter=600)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4550,16 +9544,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "52.800000000000004"
+       "np.float64(51.38461538461539)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4570,15 +9572,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-11 {\n",
+       "<style>#sk-container-id-14 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -4590,31 +9593,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-14.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-14.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 {\n",
+       "#sk-container-id-14 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 pre {\n",
+       "#sk-container-id-14 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-hidden--visually {\n",
+       "#sk-container-id-14 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4626,7 +9630,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-14 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4634,7 +9638,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-container {\n",
+       "#sk-container-id-14 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4644,7 +9648,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-14 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4660,14 +9664,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item::after {\n",
+       "#sk-container-id-14 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel {\n",
+       "#sk-container-id-14 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4675,28 +9679,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item {\n",
+       "#sk-container-id-14 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-serial {\n",
+       "#sk-container-id-14 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4714,24 +9718,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable {\n",
+       "#sk-container-id-14 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-11 label.sk-toggleable__label {\n",
+       "#sk-container-id-14 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-14 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4739,27 +9752,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-14 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-14 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4767,79 +9778,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-14 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-14 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label-container {\n",
+       "#sk-container-id-14 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-11 div.sk-estimator {\n",
+       "#sk-container-id-14 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4849,18 +9859,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted {\n",
+       "#sk-container-id-14 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-11 div.sk-estimator:hover {\n",
+       "#sk-container-id-14 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4876,23 +9886,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -4902,7 +9914,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4912,7 +9925,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -4947,12 +9961,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link {\n",
+       "#sk-container-id-14 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -4962,34 +9976,718 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-14 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
-       "                                            max_iter=600))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
-       "                                            max_iter=600))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: MLPClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
+       "                                            max_iter=600))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">MLPClassifier... max_iter=600)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: MLPClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>MLPClassifier(hidden_layer_sizes=30, max_iter=600)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=hidden_layer_sizes,-array-like%20of%20shape%28n_layers%20-%202%2C%29%2C%20default%3D%28100%2C%29\">\n",
+       "            hidden_layer_sizes\n",
+       "            <span class=\"param-doc-description\">hidden_layer_sizes: array-like of shape(n_layers - 2,), default=(100,)<br><br>The ith element represents the number of neurons in the ith<br>hidden layer.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('activation',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=activation,-%7B%27identity%27%2C%20%27logistic%27%2C%20%27tanh%27%2C%20%27relu%27%7D%2C%20default%3D%27relu%27\">\n",
+       "            activation\n",
+       "            <span class=\"param-doc-description\">activation: {'identity', 'logistic', 'tanh', 'relu'}, default='relu'<br><br>Activation function for the hidden layer.<br><br>- 'identity', no-op activation, useful to implement linear bottleneck,<br>  returns f(x) = x<br><br>- 'logistic', the logistic sigmoid function,<br>  returns f(x) = 1 / (1 + exp(-x)).<br><br>- 'tanh', the hyperbolic tan function,<br>  returns f(x) = tanh(x).<br><br>- 'relu', the rectified linear unit function,<br>  returns f(x) = max(0, x)</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27sgd%27%2C%20%27adam%27%7D%2C%20default%3D%27adam%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'sgd', 'adam'}, default='adam'<br><br>The solver for weight optimization.<br><br>- 'lbfgs' is an optimizer in the family of quasi-Newton methods.<br><br>- 'sgd' refers to stochastic gradient descent.<br><br>- 'adam' refers to a stochastic gradient-based optimizer proposed<br>  by Kingma, Diederik, and Jimmy Ba<br><br>For a comparison between Adam optimizer and SGD, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`.<br><br>Note: The default solver 'adam' works pretty well on relatively<br>large datasets (with thousands of training samples or more) in terms of<br>both training time and validation score.<br>For small datasets, however, 'lbfgs' can converge faster and perform<br>better.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=alpha,-float%2C%20default%3D0.0001\">\n",
+       "            alpha\n",
+       "            <span class=\"param-doc-description\">alpha: float, default=0.0001<br><br>Strength of the L2 regularization term. The L2 regularization term<br>is divided by the sample size when added to the loss.<br><br>For an example usage and visualization of varying regularization, see<br>:ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('batch_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=batch_size,-int%2C%20default%3D%27auto%27\">\n",
+       "            batch_size\n",
+       "            <span class=\"param-doc-description\">batch_size: int, default='auto'<br><br>Size of minibatches for stochastic optimizers.<br>If the solver is 'lbfgs', the classifier will not use minibatch.<br>When set to \"auto\", `batch_size=min(200, n_samples)`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate,-%7B%27constant%27%2C%20%27invscaling%27%2C%20%27adaptive%27%7D%2C%20default%3D%27constant%27\">\n",
+       "            learning_rate\n",
+       "            <span class=\"param-doc-description\">learning_rate: {'constant', 'invscaling', 'adaptive'}, default='constant'<br><br>Learning rate schedule for weight updates.<br><br>- 'constant' is a constant learning rate given by<br>  'learning_rate_init'.<br><br>- 'invscaling' gradually decreases the learning rate at each<br>  time step 't' using an inverse scaling exponent of 'power_t'.<br>  effective_learning_rate = learning_rate_init / pow(t, power_t)<br><br>- 'adaptive' keeps the learning rate constant to<br>  'learning_rate_init' as long as training loss keeps decreasing.<br>  Each time two consecutive epochs fail to decrease training loss by at<br>  least tol, or fail to increase validation score by at least tol if<br>  'early_stopping' is on, the current learning rate is divided by 5.<br><br>Only used when ``solver='sgd'``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=learning_rate_init,-float%2C%20default%3D0.001\">\n",
+       "            learning_rate_init\n",
+       "            <span class=\"param-doc-description\">learning_rate_init: float, default=0.001<br><br>The initial learning rate used. It controls the step-size<br>in updating the weights. Only used when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('power_t',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=power_t,-float%2C%20default%3D0.5\">\n",
+       "            power_t\n",
+       "            <span class=\"param-doc-description\">power_t: float, default=0.5<br><br>The exponent for inverse scaling learning rate.<br>It is used in updating effective learning rate when the learning_rate<br>is set to 'invscaling'. Only used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_iter,-int%2C%20default%3D200\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=200<br><br>Maximum number of iterations. The solver iterates until convergence<br>(determined by 'tol') or this number of iterations. For stochastic<br>solvers ('sgd', 'adam'), note that this determines the number of epochs<br>(how many times each data point will be used), not the number of<br>gradient steps.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">600</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('shuffle',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=shuffle,-bool%2C%20default%3DTrue\">\n",
+       "            shuffle\n",
+       "            <span class=\"param-doc-description\">shuffle: bool, default=True<br><br>Whether to shuffle samples in each iteration. Only used when<br>solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Determines random number generation for weights and bias<br>initialization, train-test split if early stopping is used, and batch<br>sampling when solver='sgd' or 'adam'.<br>Pass an int for reproducible results across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for the optimization. When the loss or score is not improving<br>by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,<br>unless ``learning_rate`` is set to 'adaptive', convergence is<br>considered to be reached and training stops.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>Whether to print progress messages to stdout.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous<br>call to fit as initialization, otherwise, just erase the<br>previous solution. See :term:`the Glossary <warm_start>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=momentum,-float%2C%20default%3D0.9\">\n",
+       "            momentum\n",
+       "            <span class=\"param-doc-description\">momentum: float, default=0.9<br><br>Momentum for gradient descent update. Should be between 0 and 1. Only<br>used when solver='sgd'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=nesterovs_momentum,-bool%2C%20default%3DTrue\">\n",
+       "            nesterovs_momentum\n",
+       "            <span class=\"param-doc-description\">nesterovs_momentum: bool, default=True<br><br>Whether to use Nesterov's momentum. Only used when solver='sgd' and<br>momentum > 0.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('early_stopping',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=early_stopping,-bool%2C%20default%3DFalse\">\n",
+       "            early_stopping\n",
+       "            <span class=\"param-doc-description\">early_stopping: bool, default=False<br><br>Whether to use early stopping to terminate training when validation<br>score is not improving. If set to True, it will automatically set<br>aside ``validation_fraction`` of training data as validation and<br>terminate training when validation score is not improving by at least<br>``tol`` for ``n_iter_no_change`` consecutive epochs. The split is<br>stratified, except in a multilabel setting.<br>If early stopping is False, then the training stops when the training<br>loss does not improve by more than ``tol`` for ``n_iter_no_change``<br>consecutive passes over the training set.<br>Only effective when solver='sgd' or 'adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=validation_fraction,-float%2C%20default%3D0.1\">\n",
+       "            validation_fraction\n",
+       "            <span class=\"param-doc-description\">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Must be between 0 and 1.<br>Only used if early_stopping is True.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_1',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_1,-float%2C%20default%3D0.9\">\n",
+       "            beta_1\n",
+       "            <span class=\"param-doc-description\">beta_1: float, default=0.9<br><br>Exponential decay rate for estimates of first moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('beta_2',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=beta_2,-float%2C%20default%3D0.999\">\n",
+       "            beta_2\n",
+       "            <span class=\"param-doc-description\">beta_2: float, default=0.999<br><br>Exponential decay rate for estimates of second moment vector in adam,<br>should be in [0, 1). Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.999</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('epsilon',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=epsilon,-float%2C%20default%3D1e-8\">\n",
+       "            epsilon\n",
+       "            <span class=\"param-doc-description\">epsilon: float, default=1e-8<br><br>Value for numerical stability in adam. Only used when solver='adam'.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-08</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=n_iter_no_change,-int%2C%20default%3D10\">\n",
+       "            n_iter_no_change\n",
+       "            <span class=\"param-doc-description\">n_iter_no_change: int, default=10<br><br>Maximum number of epochs to not meet ``tol`` improvement.<br>Only effective when solver='sgd' or 'adam'.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_fun',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neural_network.MLPClassifier.html#:~:text=max_fun,-int%2C%20default%3D15000\">\n",
+       "            max_fun\n",
+       "            <span class=\"param-doc-description\">max_fun: int, default=15000<br><br>Only used when solver='lbfgs'. Maximum number of loss function calls.<br>The solver iterates until convergence (determined by 'tol'), number<br>of iterations reaches max_iter, or this number of loss function calls.<br>Note that number of loss function calls will be greater than or equal<br>to the number of iterations for the `MLPClassifier`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">15000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-14');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=MLPClassifier(hidden_layer_sizes=30,\n",
        "                                            max_iter=600))"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5001,16 +10699,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_271755/1706611708.py:1: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "52.800000000000004"
+       "np.float64(51.815384615384616)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5029,7 +10735,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -5043,7 +10749,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/ml/winesc_multi_stacking.ipynb b/_doc/practice/ml/winesc_multi_stacking.ipynb
index 7b94f5a..9daa510 100644
--- a/_doc/practice/ml/winesc_multi_stacking.ipynb
+++ b/_doc/practice/ml/winesc_multi_stacking.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -30,7 +30,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {
       "image/png": {
        "width": 400
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -90,7 +90,8 @@
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -102,20 +103,21 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-1.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-1.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
        "#sk-container-id-1 {\n",
@@ -235,12 +237,21 @@
        "/* Toggleable label */\n",
        "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
@@ -258,9 +269,7 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -286,9 +295,9 @@
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -342,7 +351,6 @@
        "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
@@ -388,23 +396,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -414,7 +424,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -424,7 +435,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -464,7 +476,7 @@
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -476,6 +488,7 @@
        "\n",
        "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
@@ -492,7 +505,547 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1500)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1500)</pre></div> </div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneVsRestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=estimator,-estimator%20object\">\n",
+       "            estimator\n",
+       "            <span class=\"param-doc-description\">estimator: estimator object<br><br>A regressor or a classifier that implements :term:`fit`.<br>When a classifier is passed, :term:`decision_function` will be used<br>in priority and it will fallback to :term:`predict_proba` if it is not<br>available.<br>When a regressor is passed, :term:`predict` is used.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">LogisticRegre...max_iter=1500)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation: the `n_classes`<br>one-vs-rest problems are computed in parallel.<br><br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.<br><br>.. versionchanged:: 0.20<br>   `n_jobs` default changed from 1 to None</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>The verbosity level, if non zero, progress messages are printed.<br>Below 50, the output is sent to stderr. Otherwise, the output is sent<br>to stdout. The frequency of the messages increases with the verbosity<br>level, reporting all iterations at 10. See :class:`joblib.Parallel` for<br>more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\"><pre>LogisticRegression(max_iter=1500)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1500</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
       ],
       "text/plain": [
        "OneVsRestClassifier(estimator=LogisticRegression(max_iter=1500))"
@@ -516,10 +1069,18 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_272079/2645516986.py:3: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(clr.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "54.70769230769231"
+       "np.float64(54.21538461538462)"
       ]
      },
      "execution_count": 7,
@@ -572,7 +1133,6 @@
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
-       "      <th>9</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -580,70 +1140,54 @@
        "      <th>3</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>18</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>339</td>\n",
-       "      <td>198</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>343</td>\n",
+       "      <td>194</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>184</td>\n",
-       "      <td>527</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>171</td>\n",
+       "      <td>515</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>23</td>\n",
-       "      <td>210</td>\n",
+       "      <td>224</td>\n",
        "      <td>23</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>33</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>49</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -651,14 +1195,13 @@
        "</div>"
       ],
       "text/plain": [
-       "   3  4    5    6   7  8  9\n",
-       "3  0  0    3    5   0  1  0\n",
-       "4  0  0   40   18   0  0  0\n",
-       "5  0  0  339  198   0  0  0\n",
-       "6  0  0  184  527   8  0  0\n",
-       "7  0  0   23  210  23  0  0\n",
-       "8  0  0    3   33   9  0  0\n",
-       "9  0  0    0    1   0  0  0"
+       "   3  4    5    6   7  8\n",
+       "3  0  0    1    4   0  0\n",
+       "4  0  0   28   22   1  0\n",
+       "5  0  0  343  194   2  0\n",
+       "6  0  0  171  515  16  1\n",
+       "7  0  0   23  224  23  0\n",
+       "8  0  0    6   49   2  0"
       ]
      },
      "execution_count": 8,
@@ -693,10 +1236,18 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_272079/717454980.py:5: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(rfc.predict(X_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "68.9846153846154"
+       "np.float64(69.72307692307692)"
       ]
      },
      "execution_count": 9,
@@ -731,7 +1282,8 @@
       "text/html": [
        "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -743,20 +1295,21 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-2.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
        "#sk-container-id-2 {\n",
@@ -876,12 +1429,21 @@
        "/* Toggleable label */\n",
        "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
@@ -899,9 +1461,7 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -927,9 +1487,9 @@
        "\n",
        "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
        "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -983,7 +1543,6 @@
        "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
@@ -1029,23 +1588,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1055,7 +1616,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1065,7 +1627,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1105,7 +1668,7 @@
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1117,6 +1680,7 @@
        "\n",
        "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
@@ -1133,7 +1697,569 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
       ],
       "text/plain": [
        "RandomForestClassifier()"
@@ -1163,10 +2289,18 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_272079/3402916033.py:2: FutureWarning: Series.ravel is deprecated. The underlying array is already 1D, so ravel is not necessary.  Use `to_numpy()` for conversion to a numpy array instead.\n",
+      "  numpy.mean(rfc_y.predict(rf_test).ravel() == y_test.ravel()) * 100\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "66.83076923076922"
+       "np.float64(66.70769230769231)"
       ]
      },
      "execution_count": 11,
@@ -1194,7 +2328,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.7269546464802059, 0.6626763197852255, 0.6701197104064432)"
+       "(0.6219199242594116, 0.5861175474001545, 0.5618693474848643)"
       ]
      },
      "execution_count": 12,
@@ -1223,7 +2357,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Pnj1RqVRMmzYtXTmpqanpkmVu1GPIe3/IkCGo1Wp+/PFHli9fjpWVFYMGDTLaJ41837sjzW+//abtWzxq1Cjx/fffi0WLFom+ffsKpVIphg4dqt03rQtez549xaJFi7SPX+6Ct3TpUm0PhyVLlohhw4YJX19f4e7unmHvjpe7vwmRvuvWokWLtF23+vTpo7PvzJkztV3OZs+eLZYsWSI+/fRTUb58efH1119nef5ZxbBhw4Z03fBSU1PFW2+9JQDRrFkzMX/+fLF8+XLRv39/YWFhIapWrSrCw8N1ylGpVKJfv34CELVr1xYzZswQK1asEDNmzBD169cXgDh27FiWcRriiy++0L4e33zzjViwYIHo37+/GDdunHaftNe3bdu2YuHCheKDDz7ItAteRr0e0voqvwwQI0aMSFfPq36PV65cEUqlUlSvXl0sXLhQfPXVV8LPz0/UqFFDACI0NPSVdRtSxsu9O2rXri06dOggpk+fLn744Qfx8ccfCxsbG9G5c2ftPmld8EqUKCGmT58uZs2aJcqWLZtpF7yX/6bSumemdRfMzK5du3S6xn3++eeZdo177733BCDat28v5s6dKxYuXChGjRolihcvru3Lnpv16PveX7FihQDEqlWrtNt++eUXAYhFixZlGbe+CkySFkKIa9euiSFDhggfHx+hVCqFk5OTaNy4sViwYIHOQJWUlBQxZcoU4evrK6ytrUWpUqUyHMyiUqnE2LFjtZ34AwICxI0bNzLtgpdVkr506ZLo3r27cHJyEkWKFBEjR47McDDLpk2bRJMmTYSDg4NwcHAQlSpVEiNGjBBXr17N8tyzikGlUgk/Pz/h5+en00VQpVKJlStXisaNGwtnZ2dha2srqlatKqZMmZJlJ/yNGzeKtm3bCjc3N2FlZSW8vb1Fr169REhISJYxZseKFStErVq1hI2NjShSpIho3rx5uoEmCxcuFJUqVRLW1tbC09NTvP/++5kOZnmZoUlan9/j77//Ll577TVha2srfHx8xKxZs7RvZn2StCFlvJykly1bJpo1ayaKFi0qbGxshJ+fnxgzZoyIjo7WKf/s2bMiICBAODo6Cnt7e9GyZct0/2BzmqSF0Pw9V65cWdjY2IgqVapkOshECE0f7zp16gg7Ozvh5OQkqlevLj799NMMRxGbuh593vthYWHCxcVF5x9gmjfffFM4ODiIW7duvTL2V1EIYaxrckkquCZPnsyUKVN4/Pgx7u7u5g5HKkQKRJu0JElSQSWTtCRJUh4mk7QkSVIeJtukJUmS8jB5JS1JkpSHySQt6Thy5AhTp07N0RwdkiQZj0zShdTt27dRKBSsWrVKZ3udOnXYvXs3gwcPzvTYkJAQFAoFISEhpg0yn8rstTU1hUKRq3OhSLlDJmlJh52dHdu2beP8+fPaIc+SlNvWrFmDQqHQe2KxgkwmaSmdokWLsmvXLuLi4khJSTF3OFIhExcXp52PR5JJWspE2bJlGT9+vN7zWBc2z58/N3cIBdaXX36Jk5NTtqfELWhkkjaTyZMno1AouHbtGu+88w4uLi4UK1aML774AiEEYWFhdOnSBWdnZ7y8vPj222/TlfHo0SMGDRqEp6cntra21KhRg9WrV6fb79mzZwwYMAAXFxdcXV0JDAzMdGaxK1eu0L17d9zc3LC1taVOnTps3bpVr3M6efIk7dq1w8XFBXt7e5o3b87Ro0d19omNjeWjjz7Cx8cHGxsbPDw8aNOmjc7k7Fm9XleuXKFnz544OztTtGhRRo0apZ3dL01qairTpk3Dz88PGxsbfHx8+Oyzz7Tz/abJrA3Xx8eHAQMGaB+vWrUKhULBH3/8wfDhw/Hw8NBZ6UYff/31FwMGDKBs2bLY2tri5eXFu+++q/fshomJiUyePJkKFSpga2uLt7c33bp14+bNm5kec+fOHYYPH07FihWxs7OjaNGi9OjRQ2f2O4CUlBSmTJlC+fLlsbW1pWjRojRp0oR9+/Zp9wkPD2fgwIGULFkSGxsbvL296dKlS7qydu3aRdOmTXFwcMDJyYmOHTty8eJFvV+n69evM3fuXObMmYOVVYGYSTnH5KtgZr169aJy5cp89dVX7Nixgy+//BI3NzeWLVtGq1atmDVrFmvWrOGTTz6hXr16NGvWDICEhARatGjBjRs3GDlyJL6+vmzYsIEBAwbw7NkzRo0aBWimu+zSpQtHjhxh2LBhVK5cmS1bthAYGJgulosXL9K4cWO8vb0ZO3Ysjo6OrF+/nm7durF+/Xq6d++e6XkcPHiQ9u3bU6dOHSZNmoSFhQUrV66kVatWHD58mPr16wMwbNgwNm7cyMiRI6lSpQpPnjzhyJEjXL58mdq1a7/y9erZsyc+Pj7MnDmTEydO8N133xEVFaWd/xpg8ODBrF69mu7du/Pxxx9z8uRJZs6cyeXLl9myZYtBv58XDR8+nGLFijFx4kSDr6T37dvHrVu3GDhwIF5eXly8eJHly5dz8eJFTpw4keUiFCqVik6dOnHgwAF69+7NqFGjiI2NZd++ffzzzz/4+flleNyff/7JsWPH6N27NyVLluT27dssWbKEFi1acOnSJezt7QHNP8CZM2cyePBg6tevT0xMDKdPn+bs2bO0adMGgLfeeouLFy/ywQcf4OPjw6NHj9i3bx93797VzqX9888/ExgYSEBAALNmzSI+Pp4lS5bQpEkTzp07p90vKx999BEtW7akQ4cOrF+/3qDXuMDK8RRNUrakzar24hSqqampomTJkkKhUGhXGxdCiKioKGFnZ6cz817aYrK//PKLdltycrJo2LChcHR01K50vXXrVgGI2bNn69TTtGnTdIuttm7dWlSpUkVnEVS1Wi1ef/114efnp9328ixoarValC9fXgQEBOgsWBofHy98fX1FmzZttNtcXFx0Zpcz9PV61UKwub3QcEZCQ0PTvbYZLSz7v//9TwDi0KFDWZaXNvvdnDlz0j334uv98vlkVOfx48cFIH766Sfttho1amQ6G58Qmr8/IMvpcmNjY4Wrq6sYMmSIzvbw8HDh4uKSbntGtm/fLqysrMTFixeFEJophR0cHF55XEEnmzvM7MWubpaWltStWxchBIMGDdJud3V1pWLFijoLo+7cuRMvLy/efvtt7TZra2s+/PBD4uLi+OOPP7T7WVlZ8f777+vUk7aAapqnT59y8OBBAgMDUSgUJCYmkpiYSFJSEl27duXmzZvcu3cvw3M4f/48169fp0+fPjx58kS7wOfz589p3bo1hw4d0k7c7+rqysmTJ3nw4EG2Xq9XLQRrroWGX+XFlUASExOJjIzULqjwqqaeTZs24e7unu53BlkvA/dinSkpKTx58oRy5crh6uqqU6erqysXL17McHWTtHKUSiUhISGZrmu4b98+nj17xttvv62zyKulpSUNGjR45aoyycnJjB49mmHDhmW5rmVhJJO0mb28yKeLiwu2trbppsN0cXHReYPcuXOH8uXLp1tJO21JsLS1BO/cuYO3t3e6rkwvL0R648YNhBCMHTsWOzs7na+0ZbceP36c4TmkvbkDAwMpVqyYztcPP/xAUlKSdtWM2bNn888//1CqVCnq16/P5MmTM1yVOzOvWgjWXAsNv8rTp08ZNWoUnp6e2NnZUaxYMW15Wa3+DZqVhSpWrGhwG21CQgITJ06kVKlS2NjY4O7uTrFixXj27JlOnVOnTuXZs2dUqFCB6tWrM2bMGO0alwA2NjbMmjWLXbt24enpSbNmzZg9ezbh4eHafdL+Blq1apXub2Dv3r2vXOR17ty5REZGZnsdxoJMtkmbWUZXZqZe+DMjaVe6EyZM0K5r+LLM1l5MO/brr7/OdD3CtH8SPXv2pGnTpmzZsoW9e/fy9ddfM2vWLDZv3qxdgsoQmV1Jmnuh4Zf17NmTY8eOMWbMGGrWrImjoyNqtZp27dplubBsTnzwwQesXLmSjz76iIYNG+Li4oJCoaB37946dTZr1oybN2/y22+/sXfvXn744Qfmzp3L0qVLtZ/0PvroIzp37szWrVvZs2cPX3zxBTNnzuTgwYPUqlVLW97PP/+Ml5dXuliy+gcTHR3Nl19+yfDhw4mJiSEmJgbQdMUTQnD79m3s7e0zXeW7oJNJOp8qU6YMf/31F2q1Wudq+sqVK9rn074fOHCAuLg4navplxfpTFtvMDU1VfsxXF9pN66cnZ31WvzV29ub4cOHM3z4cB49ekTt2rWZPn26Xkn6VQvBmmuh4axERUVx4MABpkyZwsSJE3XORR9+fn6cPHmSlJQUg7pEbty4kcDAQJ2eQYmJiRn27HFzc2PgwIEMHDiQuLg4mjVrxuTJk3Wa4/z8/Pj444/5+OOPuX79OjVr1uTbb7/ll19+0f4NeHh4GLwAcFRUFHFxccyePZvZs2ene97X15cuXbro3cuooJHNHflUhw4dCA8PZ926ddptqampLFiwAEdHR5o3b67dLzU1lSVLlmj3U6lU6UYTenh40KJFC5YvX879+/fT1ffiR9uX1alTBz8/P7755hvi4uLSPZ/WTKJSqdJ9tPfw8KB48eLpusdl5lULwZproeGsZLSwbEYxZuatt94iMjKShQsXpnsuq09XlpaW6Z5fsGBBuvN7uRugo6Mj5cqV0/5O4uPj03Vz9PPzw8nJSbtPQEAAzs7OzJgxI8MBUJk1lYHmb2DLli3pvlq2bImtrS1btmxh/PjxmR5f0Mkr6Xxq6NChLFu2jAEDBnDmzBl8fHzYuHEjR48eZd68eTg5OQHQuXNnGjduzLhx47h9+zZVqlRh8+bNGbaDLlq0iCZNmvDaa68xZMgQ/Pz8ePjwIUePHuXhw4c67ZQvsrCw4IcffqB9+/ZUrVqVgQMHUqJECe7fv09wcDDOzs5s27aN2NhYSpYsSffu3alRowaOjo7s37+fP//8M8N+4BkJDQ3ljTfeoF27dhw/fpxffvmFPn36UKNGDQBq1KhBYGAgy5cv59mzZzRv3pxTp06xevVqunbtSsuWLbVlDR48mGHDhvHWW2/Rpk0bLly4wJ49e4y+PJazs7O2HTclJYUSJUqwd+9eQkND9Tq+f//+/PTTTwQFBXHq1CmaNm3K8+fP2b9/P8OHD6dLly4ZHtepUyd+/vlnXFxcqFKlCsePH2f//v0ULVpUZ78qVarQokUL6tSpg5ubG6dPn9Z2kwS4du0arVu3pmfPnlSpUgUrKyu2bNlCREQEvXv31p7jkiVL6NevH7Vr16Z3794UK1aMu3fvsmPHDho3bpzhPxnQrESf0cCVrVu3curUKTmoxYw9Swq1tC5ljx8/1tmeWbejjBZSjYiIEAMHDhTu7u7a1aVf7PaV5smTJ6Jfv37C2dlZuLi4iH79+olz586l6yYmhBA3b94U/fv3F15eXsLa2lqUKFFCdOrUSWzcuFG7T2YLkZ47d05069ZNuwhqmTJlRM+ePcWBAweEEEIkJSWJMWPGiBo1aggnJyfh4OAgatSoIRYvXqz366XPQrC5udBwRjLqgnfv3j3x5ptvCldXV+Hi4iJ69OghHjx4kGk3wJfFx8eLCRMmaM/Jy8tLdO/eXdy8eVO7z8tlRUVFaf8+HB0dRUBAgLhy5Uq68/vyyy9F/fr1haurq7CzsxOVKlUS06dP1662HhkZKUaMGCEqVaokHBwchIuLi2jQoIFYv359ujiDg4NFQECAcHFxEba2tsLPz08MGDBAnD59Wq/X7kWyC56GnPRfyhfkQrBSYSXbpCVJkvIwmaQlSZLyMJmkJUmS8jDZJi1JkpSHyStpSZKkPEwmaUmSpDwsXwxmUavVPHjwACcnpxzNySBJkpRXCCGIjY2lePHi6SZKe1G+SNIPHjygVKlS5g5DkiTJ6MLCwrJc6SdfJOm0Ic5hYWE4OzubORpJkqSci4mJoVSpUtr8lpl8kaTTmjicnZ1lkpYkqUB5VROuvHEoSZKUh8kkLUmSlIfJJC1JkpSHySQtSZKUh8kkLUmSlIfJJC1JkpSHySQtSZKUhxmcpA8dOkTnzp0pXrw4CoVCrxV8Q0JCqF27NjY2NpQrV45Vq1ZlI1RJkqTCx+Ak/fz5c2rUqJFu1ebMhIaG0rFjR1q2bMn58+f56KOPGDx4MHv27DE4WEmSpMLG4BGH7du3p3379nrvv3TpUnx9fbWrQVeuXJkjR44wd+5cAgICDK1ekiTJbIQQJKSo0m1XqVU42ihNMgGcyYeFHz9+HH9/f51tAQEBfPTRR5kek5SURFJSkvZxTEyMqcKTJEnK2K0QOL4YEqMBEMCV8BjiklIBSFIITjqqCHZWUTy2BPM+3Ia90vgp1eRJOjw8HE9PT51tnp6exMTEkJCQgJ2dXbpjZs6cyZQpU0wdmiRJUnqPrsC+iXBdt0lWAVQGbtpYsd7JiW2ODsRaalqMkxXhJgsnT06wNH78eIKCgrSP02aLkiRJMpm4xxAyA86sBqECCyuo+y74NCVZncruJ/8w61IwMfaR2kNKKt14y70uncoEYGdtaZKwTJ6kvby8iIiI0NkWERGBs7NzhlfRADY2NtjY2Jg6NEmSJEhJgBOL4fBcSI7VbKvYEdpM5Z6NLRuvbWTLjS08TXwK9iCEBUkXE+ng3Y65o+dioTBtT2aTJ+mGDRuyc+dOnW379u2jYcOGpq5akiQpc2o1/L0BDkyFmHuabd41UbWdxmErNevOf8vR+0cRaNbqLmZXjHt3XyPlWX3uzH+Xo68dJWVEiskvKA1O0nFxcdy4cUP7ODQ0lPPnz+Pm5kbp0qUZP3489+/f56effgJg2LBhLFy4kE8//ZR3332XgwcPsn79enbs2GG8s5AkKd9JUiXx1+O/SFGlGHikgKeh2ht6Bh5JikqgSInH4spWLJ/eBEBdtASq1/rwj1NRtp6eRnj8f23M9b1e561yPbB9XIR3zz4CoHat2uzbvSNXPvErhBDCkANCQkJo2bJluu2BgYGsWrWKAQMGcPv2bUJCQnSOGT16NJcuXaJkyZJ88cUXDBgwQO86Y2JicHFxITo6Wk76L0kFwOUnl/n00Kfcjrlt7lAypE61JzW6LslR9REp7umePx70Ot4eRXNUh755zeAkbQ4ySUtSwaAWan66+BPzz80nVZ2Kq40rXg5e+h2cFAvR9yDtytva1qC6BZCUotY+fi5siMSV1BfG9IlUB1Ki65AaWw2EdYbl1CzpxJYRTXPcJ1rfvJYne3dIklTwPIp/xIQjEzjx8AQArUq1YkqjKbjaumZ9YEoC7J8MZ5ZqHhctD92WQYk6BtUfn5xKlYmabnWnP/fHXqlfb4wffviRjz4aRd269fjtt9/wci9ikkErmZFJWpIkkztw9wCTjk0iOikaOys7Pq33KW+Vf+vVye7Bedg8BCKvaR7XGwJtpoLSPkfx2Cst9R548uHw93B3daJjx464uLjkqN7skElakiSTiU+J5+vTX7Px2kYAKrtVZlazWfi6+AKZD7NGnYrV8e+wPjwLhToV4eBJUqcFqP1aa55PTjU8luQM6snEhQsXKFOmDK6urgD06dPH4PqMRSZpSZJM4kbUDUaHjOZ2zG0UKBhQbQAf1BiJ9ekV8M9GhFBzPSKO+AwSritx+FhoxlfsVNVnwpN3iVqdCph+YrY///yTtm3bUq5cOfbt26dN1OYik7QkSUZ34M4Bxh8ZT0JqAh52HsxoOoMGHnVgRxCcXQ1ohllXgEzn4owVdkxKCWSzuum/extH3TJFMh0deOrUKdq2bUt0dDS2trZYWppmFKEhZJKWJMlo1ELNkgtLWHpBc5OvgVcDvm7+NUUU1vC/3nBjHygsoOVnJBWtzPA1ZwGY26smNlYv9LJAgaV3Lb509OBLI8doZ22ZYVv4yZMnadu2LTExMTRt2pQdO3bg5ORk5NoNJ5O0JElGEZccx/gj4wkJCwHgncrv8HHdj7F6Hgm/doGHF8DKDrr/CJU6okpO5YBakyytKgdgY4IZ5PR14sQJAgICiImJoVmzZuzYsQNHR0ezxfMimaQlScqx29G3+TD4Q0KjQ1FaKJnUaBJv+L2hmVFuTXeIDgN7d+izHkoa1nXO1E6cOEHbtm2JjY2lefPmbN++Pc8kaJBJWpKkrDx/AvdPk6JO5UxsKMnq9EO4I5Nj+ebONmJViXgoXZhfMZBqKhs4/yvsGgdJ0VC0HPTdCG6+ZjiJrBUpUgQHBwfq1KnD9u3bcXBwMHdIOmSSliQpY/fPwM/dCE+JZZSnO5deMU9FrcRE5ty9h/vVT3SfKNUA3l4L9m4mDDb7KlasyJEjR/Dy8spzCRpkkpYkKSN3jsOaHpxRJBNUsjhPLRQ4CQVlRMa9HeqrrRmpKIq1Z0ndJ0rVB//JYJ3xtMTmcvjwYRISEmjbti0Afn5+Zo4oczJJS5Kk62YwYm0f1tta8FVRT1IVUMmtEvNazqOEYwlzR5djhw4dokOHDqSmphISEsLrr79u7pCyJJO0JEn/ubaH5HX9mOHqwCZnzc2z9j7tmdJ4CnZWeetqODv++OMPOnbsyPPnz2nTpg01atQwd0ivJJO0JEkaF7fyaOsQPvYownlbGxQoGFHjQ96pHIhQKzIcGZgThgzTNoaQkBA6duxIfHw8AQEBbNmyJdPVofISmaQlSYKzP/Hn3jGM83LnkZUVQmVL/P23mXnZm5nsNXd0ORYcHEzHjh1JSEigXbt2bNmyBVtbw6Y6NReZpCWpMFOrSN47gQVX/8dqr2IIhQJ1kgfxYf0znOzeFLIapm0Mf/31lzZBt2/fns2bN+ebBA0ySUtS4ZUYzbUNfRmXeJ3rrppJ51Oi6pEY0YnTEzrqPd9yTmU2TNtYqlSpQpcuXYiJiWHz5s35bpFrmaQlqRBSR17n5009mW+TQopSiZuVA+Mbfsmw5UmAYfMt53VWVlb8/PPPqFSqfJegIdP5pyRJKqgeXNrC4M1v8I1tKikKBS3ca7G52w6alWxh7tCMZu/evbz//vuo1ZrlsqysrPJlggZ5JS1J+UtKAoQeBlXyK3eNTo3ndGyozrbwp9dZ+Pg4cTZW2AkYXWMUb1QJRKFQ5HpvC1PZs2cPXbp0ISkpiWrVqjFixAhzh5QjMklLUn4R9wh+6gqPLr5y1wdWlvQu7kVURvMhW1jgm2zD1bvvM/6KF+MLQO+NNLt376Zr164kJSXRpUsXhgwZYu6QckwmaUnKD6Lvw09d4Ml1sCsC7hUy3TUJQZDlE6IUKXgIC4rzX6K2QEET91pMP9KZzN7+pu5tYSo7d+7kzTffJDk5ma5du7Ju3TqUSqW5w8oxmaQlKa+Lug2r34Bnd8C5JAT+DkUzn2ti5rHJXLy+CRcbF37utJ7ijsV1no9PTmX6kcxXzTZ1bwtT2LFjB926dSM5OZlu3bqxdu1arK2tzR2WUcgkLUl5WeQN+OkNiLkPRXw1Cdq1dKa7b76+mU3XN6FAweyms9Ml6JcVhF4cT548oXfv3iQnJ/PWW2/xv//9r8AkaJC9OyQp74q4CCvbaxK0e0UYuCvLBH0x8iLTT0wH4INaH9CoRKPcitSsihYtypo1a+jbt2+BS9Agr6QlyTweXoDtQZASn/k+z+5Cchx4VYd+W8Eh8xGAcclxBIUEkaxOpkWpFgyqPsj4MecxycnJ2jbnN954gzfeeMPMEZmGTNKSlNuSYmF9IESFvnrfEnXgnU2am4VZ2HN7Dw+eP6CEYwlmNJmBhaJgf0jeunUrY8aMYe/evfj65r3VXoxJJmlJym27xmoStEsp6DwfLDLpSWGphJL1wPLVH993394NQPcK3XFSmn+Fa1PasmULPXv2JDU1le+++465c+eaOySTkklaknLT3xvh/BpQWEC35VAm5+3GkQmRnAo/BUCAT0COy8vLNm/eTK9evUhNTaVPnz58/fXX5g7J5Ar2ZyJJykui7mjaoQGafmKUBA2w784+1EJNdffqlHIqZZQy86KNGzdqr6D79u3L6tWrsbIq+NeZBf8MJckcVClw4wCkJvy37fhizcrZJetD87FGq2p3qKapo51Pu1fuK4TIl8O/N2zYwNtvv41KpaJfv36sXLkSy4xGUxZAMklLkinsnwzHF6bfrnSCt74HS+O89cKfh3P20VkA2vq0zXJfIQTdlx7nzJ0oo9SdW1QqFV999RUqlYr+/fuzYsWKQpOgQSZpSTK+Jzfh5DLNz6UagMW/N/4sLKDhB1DEx2hV7b2tmXejtkdtvBy8stw3IUWlk6Dzy/BvS0tL9uzZw8KFC/niiy8KVYIGmaQlyfgOTAF1Cvi1hn6bTVpVWq+O9r7tDTru9Of+FHVQ5unh37du3aJs2bIAuLu7M3nyZPMGZCbyxqEkGVPYKbj0m6b3Rttppq0qNoy/I//GQmGBfxl/g461V+bt+TnWrFlDhQoVWL58ublDMTt5JS1JxiIE7Jmg+blmX/CsatLq9tzWTJJU36s+RW2LvnI17/xyw/CXX34hMDAQtVrNmTNnzB2O2ckkLUmvkhQHGwI1Xeiyok7VDFKxtoeWE7Lc9WLkRWb9OYuoxOzfxHsU/wjQ9OrIjzcEM/Lzzz8TGBiIEIKhQ4eyZMkSc4dkdjJJS9KrXNsNN/brv3+TIHD2zvTp8OfhjDgwgieJT3IcmpO1E428W/DxnRN6H5NXbxiuXr2agQMHIoTgvffeY/HixVhYyBZZmaQl6VVuhWi+v9YbavfPel9rW/CulenTiamJjAoexZPEJ5QvUp7x9cejIPttw6WdS+No5aJ9nNH80C/Li/NFr1q1infffRchBO+//z4LFy6UCfpfMklL0quE/qH5Xr07+DTOdjFCCCYfn8ylJ5dwtXHlu5bfUdKpZI7De7EtOr/ODx0aGooQguHDh7Nw4cI890/EnPLfb1OSctPTUM2UoRZWULphjopadXEVO27twFJhyZwWc4ySoAuKyZMnU69ePTp27CgT9EtkkpakrKRdRZesBzaO2S7m8L3DzD2jma1tbP2x1POql+m+QggSUvTviZFfem28bNu2bbRu3Rp7e3sUCgWdOnUyd0h5kkzSkpSV0EOa777Nsl1EZEIk4w6PQyB4q/xb9K7YO9N98+vQbUMtX76c9957j5YtW7Jr1y5sbGzMHVKeJVvmJSkzQryQpJtnu5gZJ2cQkxxDZbfKTGgwIcuP8y8P3TZEXu218bKlS5fy3nvvAVCzZs0CsaK3KckraUnKzKNL8Pyxpt9zycybJ7Ky9/Ze9t3Zh5XCimmNp2GtxwT+afTpqfGivNhr42WLFy9mxIgRAAQFBfHNN9/k+ZjNLVtX0osWLcLHxwdbW1saNGjAqVOnstx/3rx5VKxYETs7O0qVKsXo0aNJTEzMVsCSlGtu/dseXbohWBl+tfcs8RnTT2oWhn23+rtUdKto0PFpPTX0/crryW7RokXaBP3JJ5/IBK0ng5P0unXrCAoKYtKkSZw9e5YaNWoQEBDAo0ePMtz/119/Zdy4cUyaNInLly/z448/sm7dOj777LMcBy9JJpV20zCb7dGz/5zN08SnlHUpy3uvvZflvpp5nlPz7U3AV1m2bBkjR44E4NNPP2X27NkyQevJ4OaOOXPmMGTIEAYOHAho2pd27NjBihUrGDduXLr9jx07RuPGjenTpw8APj4+vP3225w8eTKHoUuSCaUkwu2jmp/L6tceLYRg6ompnHhwAoHgftx9FCiY0mgKSsvMr8QLw83CevXq4erqynvvvcfMmTNlgjaAQUk6OTmZM2fOMH78eO02CwsL/P39OX78eIbHNGrUiF9++YVTp05Rv359bt26xc6dO+nXr1+m9SQlJZGUlKR9HBMTY0iYkpRz+yZCciw4eYPXa3odEhEfwcZrG3W29a/Sn5oeNbM8LqObhfnlJqC+ateuzd9//02JEiVkgjaQQUk6MjISlUqFp6enznZPT0+uXLmS4TF9+vQhMjKSJk2aIIQgNTWVYcOGZdncMXPmTKZMmWJIaJJkPNf2wKl/J+1/Y2Hmq3m/5NKTSwD4OPswrfE0lJZKKrlVMqjqtJuF+eEm4KssXLiQOnXq0LChZhBQyZJy8E52mLwLXkhICDNmzGDx4sWcPXuWzZs3s2PHDqZNy3yu3fHjxxMdHa39CgsLM3WYkqQRGwFbh2t+bvA+lNd/nubLTy8DUKNYDWp61KRK0SpYKAx7i6XdLMzvCfrbb7/lgw8+ICAgQL5/c8igK2l3d3csLS2JiIjQ2R4REYGXV8ZL93zxxRf069ePwYMHA1C9enWeP3/O0KFDmTBhQoaTqNjY2MjO7VLuU6th6/sQHwme1cB/skGHX36iSdKVi1Y2QXD5xzfffMOYMWMA+Oijj+QVdA4ZlKSVSiV16tThwIEDdO3aFQC1Ws2BAwe0d25fFh8fny4Rp61RJoTIRsiSlAO3j8KzTOaFfvgX3DwAVrbw1o+aGe0MkJakqxbNeLL/zIZ7F6QeHbNnz2bsWM1K6JMmTSq0S14Zk8G9O4KCgggMDKRu3brUr1+fefPm8fz5c21vj/79+1OiRAlmzpwJQOfOnZkzZw61atWiQYMG3Lhxgy+++ILOnTsXugUlJTML+xNWdXj1fgEzwMOwtuTIhEgeJTxCgYIKRSqke74w9OD46quvtJ0KJk+ezKRJk8wcUcFgcJLu1asXjx8/ZuLEiYSHh1OzZk12796tvZl49+5dnSvnzz//HIVCweeff879+/cpVqwYnTt3Zvr06cY7C0nSx9F5mu9Fy2e+YneJ2lD3XYOLTruK9nXxxd7aPt3z+gz3zs89OtavX69N0FOmTGHixIlmjqjgUIh80OYQExODi4sL0dHRODs7mzscKT+KvAEL6wICRpyCYoaN/nuV5X8tZ8G5BXQs25Gvmn6V7vn45FSqTNSsSZjZcO/83KMjMTGRN998k8aNG/P555+bO5x8Qd+8JufukAqH4wsAARXaGT1Bwws3Dd1efdMwv07MnxEhBAqFAltbW7Zt24aVVcE4r7xEvqJSwRf3CM7/T/Nzow9NUkVa97sqRasA6W8SFqSbg2mmTp1KTEwMX3/9NQqFQiZoE5GvqlTwnVoOqiQoUQfKNDJ68dFJ0dyPuw9ARbeKheIm4ZQpU7Q9Nzp27EjLli3NG1ABJueTlgq2xGg4uVzzc6MPwQRtvmlX0aWcSuGsdM7yJmF+vjkImk8IL3atmzVrlkzQJiavpKWC7dRySIoG94pQ+Q2TVJFVe/TLNwnz883BtASdNlr466+/5pNPPjFzVAWfTNJSwZUUC8cXaX5uNgYyGN1qDFmNNCwoNwmFEHzxxRfarrPffvstQUFBZo6qcMj/fz2SlJk/f4SEKHDzg2rdTFbNpaeaiZWquFUxWR3mdv78eWbMmAFopisePXq0mSMqPGSSlgqm5Hg4vlDzc9OP9Z7JzlBxyXHcidEMM69UtNK/k/cXvJ4ctWrVYtWqVURFRTFq1Chzh1OoyCQtFUznftasT+haBl7rabJq0qYn9XLwoohNkQLVq0MIQWxsrHagRf/+/c0cUeEke3dIBY8q9b+r6MYfggGLvxrqVLhmfc9aHrXS9erIzz05hBCMHTuWBg0aEB4ebu5wCjV5JS0VPJd/g2d3wb4o1Oxr0qpOPtQsA/e69+s6209/7k9RB2W+7MkhhGDMmDF8++23AOzfv5933nnHzFEVXjJJSwWLEHD0O83P9YeCtZ3Jqnqe8px/Iv8BoIF3A53n7JX5s6udEIJPPvmEOXPmALB48WKZoM1MJmmpYLl9GB6eBys7qDfEqEW/PNT72INTpIpUSjiWpIjSM9/fMBRCEBQUxLx58wBYsmQJw4YNM29QkkzSUgHxLAz+1xuehmoe1+oLDkWNVnxGQ71tPLajLAqhYd7aGe7yKyEEo0ePZv78+QAsW7aMoUOHmjkqCWSSlgqKk0shQtP0gLU9NBxh1OIzGupt6XATAFV8OZ3t+fGGYVRUFNu3bwdg+fLlDBli3E8hUvbJJC3lf6lJcOHfWe46zYNKncCxmMmqO/25P0nqaNpteQhAyMjBuNm6aZ/Pj0O/3dzcCA4O5siRI7z99tvmDkd6geyCJ+V/V3dC/BNw8oZa/UyaoEFzU/Cfp+cAKF+kPCWdPbBXWmm/8kuCVqvVnD59Wvu4VKlSMkHnQTJJS/nf2Z8032v2Bcvc+XCY1vWugVeDV+yZN6nVakaMGMHrr7/Ohg0bzB2OlAXZ3CHlb1F34Gaw5ufa/YxSZEarep+L+Btr1xMAbLoezeH7h4H0Xe/yA7Vazfvvv8/y5ctRKBTEx8ebOyQpCzJJS/nb+TWAgLItMl9c1gAZ9eKwcjmNrfcmbL01y4HOPr0VAEuFJXU96+a4ztykVqsZNmwY33//PQqFgtWrV9Ovn3H+uUmmIZO0lH+pVXDuF83PtY0zr8TLvTisXP7E1nszCoUgNd4HV6Ubr5d1Q6FQ0LREUxyVjkapNzeo1Wree+89fvjhBywsLFi9erUcqJIPyCQt5V83DkDMfbArounRYWST+8by7dlNAHQv34tP6ozNVzcGX6RWqxk6dCg//vgjFhYW/Pzzz/Tp08fcYUl6kElayr/OrtZ8r/E2WNkYtWhr15N8e3YLAH0r92VsvbH5MjmnUSgUKJVKLCwsWLNmDb179zZ3SJKeZJKW8qfYCLi2W/NzLcPbVDO6OQiaVb2tixzH1us3APpV6ceYumPydYIGTZJeuHAhAwcOpF69euYORzKATNJS/nNmNWz7UPNzyXrgadiKKFmt5m3lfBa7EpoE3adS/k7QKpWKJUuWMHToUO1VtEzQ+Y/sJy3lL0LA4W//e/z6+wYXkelq3pbPsfXUDI0ukuLP2Hqf5OsEPWDAAD744APeeecdhBDmDknKJnklLeUvERfhmWa5KkaeBvfyOSruxdW8Z5+eyabr8fi5lGND59lYmGjhWlNLTU0lMDCQX3/9FUtLS3r27Jlv/9lI8kpaym+u7tR8r9A+xwka/lvNOyzuJltubARgwuufYW3C1VxMKTU1lf79+/Prr79iZWXFunXr6N69u7nDknJAXklL+cuVHZrvlToarUghBDNOzkAt1AT4BFDPK3+226amptKvXz/Wrl2LlZUV69ev58033zR3WFIOySQt5R/R9zQT+qOACu2MVuzu27s5++gstpa2fFznY6OVm9uGDh2qTdAbNmyga9eu5g5JMgLZ3CHlH1d3ab6XamC0me5UahXfndUstzWo+iC8Hb2NUq45BAYG4urqysaNG2WCLkDklbSUf2ibOjoYrcjjD49xL+4ezkpnAqsGGq1cc2jevDmhoaG4urqaOxTJiOSVtJQ/JDzTrF8IRh0CvuHaWgC6le+GnZXpFq01hZSUFIYOHco///yj3SYTdMEjk7SUP9zYD+pUcK8IRf2MUqRC+ZgT4cdQoKBnxZ5GKTO3JCcn06tXL77//nvat29PYmKiuUOSTEQ2d0j5gxGbOtLGdSiLHAegecnmlHIqleNyc0tagt66dSs2NjYsX74cW1tbc4clmYhM0lLel5qkuZIGqJizrndCCHosPQ6KZKxdzgDwdqX8s2RUcnIyPXv25LfffsPGxoatW7fSrp3xerpIeY9M0lLed30fJMVo1jAsUSdHRSWkqLj0MAZL+zsoLJPwsvfi9eKvGylQ00pKSqJHjx5s27YNGxsbfvvtNwICAswdlmRisk1ayvv+Wqf5Xr07GGmotqXdPQBqetTEQpE/3gbTpk1j27Zt2Nra8vvvv8sEXUjkj79OqfBKePbflKSv9TJasRZ2YQBUd69utDJNbezYsfj7+7Nt2zbatm1r7nCkXCKbO6S87dJvoEoGjyrgWc1IhQos05J0sbydpFUqFZaWmgmgnJyc2Lt3r5wsqZCRV9JS3vbXes3313pCDpOTEIL4ZBUKq2gsrGKxVFhSya2SEYI0jcTERDp16sTMmTO122SCLnzklbSUdyU8gztHND9Xy9lMbi9O9G/lpLmKLudaPs8OYElMTKRr167s2bOHQ4cO0bdvX0qXLm3usCQzkFfSUt719Jbmu6MnuOasH/OLE/1b2GpuGr6WR5s6EhIS6NKlC3v27MHe3p6dO3fKBF2IySQt5V1pk/sX8TFqsQ0qxwF586ZhWoLeu3cvDg4O7Nq1i+bNm5s7LMmMZHOHlHdF/ZukXcsYsVA116IuA1DN3Vg3Io0jPj6eLl26sH//fm2Cbtq0qbnDkswsW1fSixYtwsfHB1tbWxo0aMCpU6ey3P/Zs2eMGDECb29vbGxsqFChAjt37sxWwFIhEnVb890IV9JpQ8EtbB4RnxqPvZU9ZV3K5rhcY9q1axf79+/H0dGR3bt3ywQtAdm4kl63bh1BQUEsXbqUBg0aMG/ePAICArh69SoeHh7p9k9OTqZNmzZ4eHiwceNGSpQowZ07d+RsXdKraZs7cnYlrR0KDlja3QagqntVLC0sc1Susb311lssXLiQmjVr0rhxY3OHI+URBifpOXPmMGTIEAYOHAjA0qVL2bFjBytWrGDcuHHp9l+xYgVPnz7l2LFjWFtr1o3z8fHJWdRS4ZB2JZ3D5o60oeBYJGDvGYwaaFS8UY7DM4bnz5+TnJxMkSJFABgxYoSZI5LyGoOaO5KTkzlz5gz+/v7/FWBhgb+/P8ePH8/wmN9//52GDRsyYsQIPD09qVatGjNmzEClUmVaT1JSEjExMTpfUiGjVsEzTVc5Y904tPHcgdoiGh9nH96p/I5RysyJuLg4OnToQJs2bXj27Jm5w5HyKIOSdGRkJCqVCk9PT53tnp6ehIeHZ3jMrVu32LhxIyqVip07d/LFF1/w7bff8uWXX2Zaz8yZM3FxcdF+lSqVf6aRlIwk9iGoU8DCGpyL57g4S4frKF1Po0DBlEZTsLUy79SeaQn60KFDXL9+nVu3bpk1HinvMnkXPLVajYeHB8uXL6dOnTr06tWLCRMmsHTp0kyPGT9+PNHR0dqvsLAwU4cp5TVpTR0uJSGHbcfxKfHYem0CoHv5ntT2rJ3D4HImNjaW9u3bc/jwYVxcXNi3bx+1a5s3JinvMqhN2t3dHUtLSyIiInS2R0RE4OXlleEx3t7eWFtba+cfAKhcuTLh4eEkJyejVCrTHWNjY4ONjY0hoUkFTZRhfaSFECSkqIhLjmVH6DYSVf+tVHL+0QUslM9QJ7syvMaHJghWf2kJ+ujRo9oEXa9ePbPGJOVtBiVppVJJnTp1OHDggHY1YrVazYEDBxg5cmSGxzRu3Jhff/0VtVqNxb/TTF67dg1vb+8ME7QkAQb17HhxyLeNx3aURY9kuF9ieDfsre2NGaVBYmJiaN++PceOHcPV1ZV9+/ZRt25ds8Uj5Q8GN3cEBQXx/fffs3r1ai5fvsz777/P8+fPtb09+vfvz/jx47X7v//++zx9+pRRo0Zx7do1duzYwYwZM+RdbClrBvSR/m/It8DKSTNQJTW2EsnP6mq/Eh+8RS33BthZm6/b3ePHjwkNDaVIkSLs379fJmhJLwZ3wevVqxePHz9m4sSJhIeHU7NmTXbv3q29mXj37l3tFTNAqVKl2LNnD6NHj+a1116jRIkSjBo1irFjxxrvLKSCJxujDRXKSCyUT7C2sCZ44I/prprtrC3NOoucn58fwcHBPH/+XLZBS3rL1rDwkSNHZtq8ERISkm5bw4YNOXHiRHaqkgor7ZW0/knayvEKAHU96+Lu4GyCoAwXHR3N33//TZMmTQCoWLGimSOS8hs5wZKU54jkeIjTdOmMdyhFfHLqK740fe7TknSzks3MFvuLnj17Rtu2bfH392ffvn3mDkfKp+QES1KeIoRg1LJtfAfECjuqf3UK0KOJwiIRS/tQIG8k6aioKNq2bcvp06cpWrRohlMmSJI+5JW0lKckpKiIfngTgHvCHb0SNGBpE45CocbT3pPSzuadezkqKoo2bdpw+vRp3N3dOXjwIDVq1DBrTFL+Ja+kpTynhCISAL9ylbnUW78Vsbff+p1pJ8HXxdeUob3S06dPadOmDWfPntUm6OrV89681VL+IZO0lOcU/zdJK4qUwl6p35/ow3jNqNQyzsace9ow0dHR+Pv7c+7cOYoVK8bBgwepVi1vzVkt5T+yuUPKc9KupIVzSb2PuROj6bJnziTt4OBAxYoV8fDwIDg4WCZoySjklbSUpwjxQpJ20X9irbyQpK2srPj555+5d++enI5XMhp5JS3lGWmT8xdXPNE8dtHvSlot1NyNuQvkfpKOjIxk6tSpqNVqQJOoZYKWjEleSUt5RkKKiqsPo/CyeQqATVH9Eu6j+EckqhKxUlhR3DHn05rq6/Hjx7Ru3Zq///6b2NhYvv7661yrWyo85JW0lKd4EoWVQo2wVKJwzHhmxZelNXWUcCqBtYW1KcPTevToEa1ateLvv//G29ubwYMH50q9UuEjr6SlPOW/m4YlUFjodw2R2+3RaQn64sWLeHt7ExwcLId7SyYjk7RkNmlzQKeJT1bpJGl9pSXp0k6mH8QSERFBq1atuHTpEsWLFyc4OJgKFSqYvF6p8JJJWjKLF+eAftFwS8N7dqTdNPRx9jFafBlRqVS0a9eOS5cuUaJECYKDgylfvrxJ65Qk2SYtmcV/c0DrKvnvlbSlq/5JOjRGM2dHGRfTNndYWloyZcoUypYtS0hIiEzQUq6QV9KS2Z3+3B97pWYyfpu1P8ItULjq13SRpEoiLFYz2tDPxc9kMaZ54403aNeunVxVSMo18kpaMjt7pSX2SivslVZYxtzTbNTzSvp29G3UQo2T0gl3O3ejx/bgwQPatGmjs5q3TNBSbpJJWso7hIDof1eG17NNOjRa09Th5+Jn9FVX7t+/T4sWLdi/fz8DBgxACGHU8iVJHzJJS3lHQhSkxGt+1rN3x81ozbSmZV3LGjWU+/fv07JlS65fv06ZMmVYvXq1WZfekgovmaSlvCPyuua7oydY2+p1yM1n/yZpF+Ml6Xv37tGiRQuuX7+Oj48PISEh+PqadwpUqfCSSVrKO24f0nwvVV/vQ24907QV+7ka56ZhWFgYLVq04MaNG/j6+hISEiLn4pDMSvbukPKOW39ovvs212v3FHUKd2I1A1mMdSU9atQobt68qU3QpUubd5UXSZJJWsobUhIg7JTmZz2TdFhsGKnqVOys7PBy0G+ej1dZvnw5Qgi+++47SpXSv6+2JJmKTNJS3hB2ElRJ4OQN7rqDRIQQpIrUdIdcj9K0YZd1KYuFIvstd/Hx8djb2wPg7u7Oli1bsl2WJBmbTNJS3vBiU8cLvSiEEAzZN4STD09memhO2qNv375Nq1atGDt2LO+99162y5EkU5E3DqW84e5xzXefJjqb78TcyTJBWymsaFGqRbaqDA0NpUWLFoSGhjJnzhwSExOzVY4kmZK8kpbMT5UM989qfi79us5TpyNOA1DLoxYLWi1Id6jSUomdlZ3BVd66dYuWLVty9+5dypcvz8GDB7G11a/bnyTlJpmkJbOzCL+gaY+2c4Oi5XSeOxNxBoB6XvVwsXExSn03b96kZcuWhIWFUaFCBYKDgylePPdWdJEkQ8gkLZmdxb0/NT+UapCuPTrtSrquZ12j1HXz5k1atGjBvXv3qFixIsHBwXh7exulbEkyBZmkJZN7eXJ/0Ezwn8bi3r9d70o30NnnwfMHhD8Px0phRY1iNYwSy9atW7l37x6VKlUiODgYLy/jdN2TJFORSVoyqcwm939hDyzTknQp3SR9OlxzFV3FvQr21vZGiScoKAilUkmPHj1kgpbyBdm7QzKpzCb3T9OhZDKK5xFgYQ3Fa+k8l9YendOmjlu3bhEfr5m4SaFQ8MEHH8gELeUb8kpayjUvTu6fxu7yJtgCeNcAa91eGmnt0XU862S7zqtXr9KyZUsqVarE9u3btYNWJCm/kElayjVpk/vrCPu3D/RLXe8inkcQFhuGhcKC2h61s1XflStXaNmyJeHh4bi7u+uMLJSk/EImackk0m4WvniDMENp83X8O/NdkioJtVBzMlyTvCu5VcJR6Whw/ZcvX6ZVq1aEh4dTvXp1Dhw4gLu78VdukSRTk0laMrpX3yz8V2IMPLqo+blUA+aemcuKf1bo7JKdpo5Lly7RqlUrIiIieO2112SClvI1maQlo8voZmHdMkWws9Ztj+ZWCAg1uPlxRySx+uJqnaftrOzo4NvBoLovXbpEy5YtefToETVq1ODAgQMULVo0O6chSXmCTNKSSaXdLLSztky//NT1PZrvFQJYfH4xKqGiSYkmfNv8WwCsLayxtrQ2qL6kpCRSUlKoWbMm+/fvlwlayvdkkpZMKsObhQBqNVzfB8C14tXYdW4WAB/W+jBHfaJr1apFSEgIJUuWxM3NLdvlSFJeIZO0pJeMRg1m5pU3CwHCL0BcBCgdWfj4OAJB2zJtqVy0ssGx/fXXXzx//pyGDRsC8NprrxlchiTlVTJJS6+k941AfTyP5NAfkzn1+By4uZJUxJfge39gobBgRK0RBhd34cIFWrduTXJyMsHBwdSpk/0+1ZKUF8kkLb3Sq0YNZiajm4VJx+YTFBFMkoUFuDiD+gkAncp2MnidwvPnz+Pv78+TJ0+oV68efn7GWYxWkvISmaQlg2Q0ajAzGd0svBV+jiQLCxywoKdrNSheCztrB/pU7mNQHOfOncPf35+nT59Sv3599uzZg6urq0FlSFJ+IJO0ZJBMbwTq6VrsHXCAqq7lCeqyJltlnD17Fn9/f6KiomjQoAF79uzBxcU4c01LUl4jJ1iSck/yc66lxgBQwb1atoq4fPmyNkG//vrr7N27VyZoqUCTV9JSOi/35NCrt4Y+Iq9xTanp91zeI3s9MHx9fWnQoAHR0dHs3r0bZ2dn48QmSXlUtq6kFy1ahI+PD7a2tjRo0IBTp07pddzatWtRKBR07do1O9VKuSCtJ0eViXu0X3W/3G+cwh9f5ZpSCUCFIhWyVYStrS1btmxhz549MkFLhYLBSXrdunUEBQUxadIkzp49S40aNQgICODRo0dZHnf79m0++eQTmjZtmu1gJdPLqidHhkO7DRAZfoGnlpYoAD9X/XtinDp1ii+++AIhBKBJ1E5OTtmOQ5LyE4ObO+bMmcOQIUMYOHAgAEuXLmXHjh2sWLGCcePGZXiMSqWib9++TJkyhcOHD/Ps2bMcBS3ljpd7cmQ4tNsA1yL/AaCM0lXvFb5PnjxJ27ZtiYmJoXjx4rz//vvZrl+S8iODrqSTk5M5c+YM/v7+/xVgYYG/vz/Hjx/P9LipU6fi4eHBoEGD9KonKSmJmJgYnS8p96X15Ej7ykmCBrgeexeA8s4+eu1/4sQJ2rRpQ0xMDE2bNqVfv345ql+S8iODrqQjIyNRqVR4enrqbPf09OTKlSsZHnPkyBF+/PFHzp8/r3c9M2fOZMqUKYaEJhkos2HeRrtJ+LKURK6pYgEHKuixqOzx48cJCAggNjaW5s2bs337dhwdDZ9XWpLyO5P27oiNjaVfv358//33Bs3nO378eIKCgrSPY2JiKFWqlClCLJSMOsxbX09vcs1a07OjgmfWK60cPXqUdu3aERcXR4sWLdi+fTsODg65EaUk5TkGJWl3d3csLS2JiIjQ2R4REZHhwp43b97k9u3bdO7cWbtNrVZrKray4urVqxkO5bWxscHGxsaQ0CQD6DPMO6c3CV+mfnSJW/8m6fJFyme639OnT+nYsSNxcXG0atWKbdu2ySWvpELNoCStVCqpU6cOBw4c0HajU6vVHDhwgJEjR6bbv1KlSvz999862z7//HNiY2OZP3++vDrOAzIb5p3Tm4QvexLxF8kWCiwAL8fMV+p2c3NjwYIF/PLLL2zZskUmaKnQM7i5IygoiMDAQOrWrUv9+vWZN28ez58/1/b26N+/PyVKlGDmzJnY2tpSrZruyLK0+RVe3i6ZR06HeevrYeQlAIpZOWBtkX4ifyGE9p9Cv379eOedd4z6T0KS8iuD3529evXi8ePHTJw4kfDwcGrWrMnu3bu1NxPv3r2LhYUcbS7pehB9B2yhuJ1HuucOHTrExx9/zLZt27TNZjJBS5JGti6hRo4cmWHzBkBISEiWx65atSo7VUo59GJvDpP14MiMKoWHCY/B1hlvlzI6T/3xxx906NCB+Ph4pk6dyuLFi3M3NknK4+TcHYWAWXpzvOhpKA8tNFfG3i+MNAwODqZTp07Ex8cTEBDAt99+a574JCkPk+0ShUBmvTmM3YMjU0+u89BKU09xx+IAHDx4kI4dOxIfH0+7du3YunUrdnb6jUKUpMJEXkkXMi/25jB2D45MPbnJQyvNn5qXgxf79++nc+fOJCYm0qFDBzZt2oStra3p45CkfEgm6UImt3pz6Hhygwf/JmlPO096BPUgMTGRjh07smnTJtknXpKyIJs7CoF/J48zm7gn14m11PyplXQuyY4dOxg+fLhM0JKkB3klXcAJIeixNPPJr3LDw+hQKGKNs5UDDtYOOJRyYNGiRWaNSZLyC3klXcAlpKi49FAzi2AVb+fcuVH4oqQ4HiZpblraq+ToQUkylEzShciGYQ1zfZDIkW0/a28axj1M0E7cL0mSfmSSLkRyexDfzp07WTwtiAf/dr/r1KyTHEkoSQaSSVoyie3bt/Pmm2/i66LWXkmXdC5p5qgkKf+RSbqAEkIQn5ya+0PAgW3bttGtWzeSk5NpU9tXp4+0JEmGkb07CiBzDwM/ePAgKSkp9OjRg2ZVnzLN4j4AxR2KmyUeScrPZJIugDIaBp5rQ8DRLFb82muv0a9fP8IW1uBRESusFJb4uvjmSv2SVJDIJF3ApQ0DN/UQ8KNHj1KvXj2USiUKhUIzv7gQHOI54ESdolVxVMo1CiXJULJNuoBLGwZuygS9adMmWrRoQa9evUhOTv7viaRYDtlqrgOalmppsvolqSCTSboAys2uyBs3bqRXr16kpqbi6OiIpeV/TSrPo+9y+t+Jk5qX8c+9oCSpAJFJuoDJzWHgGzZsoHfv3qhUKvr168eqVat0kvSJe4dIVSgorQIfF59ciUmSChqZpAuY3BoGvm7dOt5++21UKhWBgYGsXLlSJ0EDbAg7AEAz5HBwScoumaQLMFMNA1+/fj19+/ZFpVIxcOBAfvzxx3QJ+kzEGY5GX8NKCN62lYNYJCm7ZO+OAsxU9wo9PT2xsbGhd+/efP/99+kWHhZC8N3Z7wB4MzaO0mVKmCYQSSoEZJKWDNa8eXP+/PNPKlWqlOHK8MceHOPso7MoseC9ZzFQpZgZopSkgkE2d0h6Wbt2LX///bf2cZUqVTJM0EII5p+dD0BvK3c8VSpwkElakrJLJmnplX766Sf69OlDq1atCAsLy3Lfw/cPc/npZeyt7BmUbK3Z6CiTtCRll0zSUpZWr17NgAEDEELw1ltvUaJE1u3Lf4b/CUDHsh1xe/5Us1FeSUtStskkLWVq5cqVDBw4ECEE77//PosXL86wieNFl59cBqC6e3V4/kizUSZpSco2maSlDK1YsYJBgwYhhGD48OEsWrTolQlaCMGlp5cAqOxaHhL+neRJJmlJyjbZuyMfEkKQkJLxPNHGmD/6t99+Y9CgQQCMHDmS7777Tq/+1vfi7hGbHIu1hTV+ShfNRoUF2LnlOCZJKqxkks5ncmOu6FatWtGoUSPq1KnD/Pnz9R4Qk9bUUV5hi/WucZqN9u7wiitwSZIyJ5N0PpPRXNEZycn80U5OTuzbtw87OzuDRixefqpJ0pWjHsKTi5qNRXyyFYMkSRoySedjaXNFZ8TQ+aOXLVvG06dPGT9+PAD29obPt5F2JV0lORnqDICi5aBiB4PLkSTpPzJJ52Npc0Xn1JIlSxg+fDgA9evXp3Xr1gaXIYTg8pN/bxomJUPzseAsl8uSpJySjYWF3KJFi7QJ+uOPP6ZVq1bZKiciPoKnSVFYCkF5axdw8jZmmJJUaMkr6Twso14cxlz9e8GCBXz44YcAjBkzhlmzZmV71ry0pg6/5BRsvWuYbnYnSSpkZJLOo0zdi2P+/Pl89NFHAIwdO5aZM2fmaFrTK0+vAFA5ORn8ahgjREmSkM0dedarenHkpPfGhQsXtAl63LhxOU7QAPfj7gPgk5IK3jJJS5KxyCvpfCCjXhw5Wf27Ro0azJs3j0ePHvHll18aZWGAx/ERALirVDJJS5IRySSdDxirF0dycjJKpRKAUaNG5bi8Fz2O1VxJF7OwBdcyRi1bkgoz2dxhYkII4pNTs/FlvBuEAN988w2NGjUiKso0bdyRCZEAuBetIG8aSpIRyStpE8qNIdz6+Prrr/n0008BzQrfQ4cONV7hF7eScvl3olQJABTzeM14ZUuSJJO0Kek7hDsrOblBCDBr1izGjdPMozF58mTjJuioO7B5CE9QQekSWAmBa5nGxitfkiSZpHNLVkO4s5KTG4QzZ87ks88+A2DKlClMnDgxW+Vk6sBUUCXzuORrwDPclS5YVOps3DokqZCTSTqXGOvmn75mzJjBhAkTAJg2bRqff/65cSu4dwb+2QgoeFxvIJyfSzGXMnLGO0kyMpmkC6CnT5+yaNEiAKZPn669mjYaIWDvv0m/xttE2joC4G7nbtx6JEmSSdqUhDBPvW5ubgQHB7N7927tsG+jurIDcfcY92wcSKkXyI37BwEoZidXYJEkY5NJ2kSEEPRYejxX67xx4wblypUDoEKFClSoUMH4lQgBB6cxt4grK12d4cAQAGwtbClpV5LExETj1ylJ+ZC1tTWWltm/6Z8mW0l60aJFfP3114SHh1OjRg0WLFhA/fr1M9z3+++/56effuKff/4BoE6dOsyYMSPT/QuKhBQVlx7GAFDF2zlHPTReRQjB5MmTmTVrFr///jtt27Y1WV1EXkc8vsL2UppVw52tnQkoGkAjt0Z4WHkQGhpqurolKZ9xdXXFy8srR6N6DU7S69atIygoiKVLl9KgQQPmzZtHQEAAV69excPDI93+ISEhvP322zRq1AhbW1tmzZpF27ZtuXjxIiVKlMh24PnJhmENjTL0OiNCCCZNmsS0adMAuHjxommT9PU9XFZa89jKEjsrO9Y1X0dsTCweHh7Y29ub7DwlKT8RQhAfH8+jR48A8PbOwdS9wkD169cXI0aM0D5WqVSiePHiYubMmXodn5qaKpycnMTq1av1rjM6OloAIjo62tBwzeZ5UoooM3a7KDN2u3ielGKSOtRqtfj8888FIADx7bffmqQeHSs7iiVzS4pqq6qJjw98LC5duiQiIyNNX68k5UORkZHi0qVLIjU1Nd1z+uY1g/pLJScnc+bMGfz9/bXbLCws8Pf35/hx/dpf4+PjSUlJwc0t8xWkk5KSiImJ0fnKT4QQRh/WnVEdn3/+OV9++SUAc+bMISgoyKR1khgNd49z2M4OgKbFmwLZW2pLkgqDtPdGSkpKtsswqLkjMjISlUqFp6enznZPT0+uXLmiVxljx46lePHiOon+ZTNnzmTKlCmGhJZniFwYCi6E4LPPPuOrr74CYN68eUafMIknN+HQ15CS8N+2+Cc8Rc3fNjYA1PWsS0x4jGzikKRMGOO9kau9O7766ivWrl1LSEgItra2me43fvx4navCmJgYSpUqlRsh5tjLQ8FzOqw7I0II7t69C8B3333HBx98YNTyAc1owktb0292ckAooLJbZYraFSWG/PUpR5LyG4OStLu7O5aWlkREROhsj4iIwMvLK8tjv/nmG7766iv279/Pa69lPQmPjY0NNv9ereVnpz/3p6iD0uhXmhYWFqxevZp+/frRrl07o5YNaJo1ru7S/NxyAtgV0T61/e5meH6P9r7tjV+vJEnpGNQmrVQqqVOnDgcOHNBuU6vVHDhwgIYNG2Z63OzZs5k2bRq7d++mbt262Y82n7FXZn/ejZcJIVi3bh0qlaat28rKyjQJGuDS76BKgmKVoNkYqD8E6g/hfpUOnH1+DwWKApGkw8LCePfddylevDhKpZIyZcowatQonjx5YtR6PvjgAypXrpzhc3fv3sXS0pLff//9leX4+PigUChQKBTY29tTvXp1fvjhB6PFuWrVKlxdXY1WXnbrGTBggPY8ra2t8fX15dNPPy20ffANnmghKCiI77//ntWrV3P58mXef/99nj9/zsCBAwHo378/48eP1+4/a9YsvvjiC1asWIGPjw/h4eGEh4cTFxdnvLMo4IQQfPzxx/Tu3ZuhQ4ciTD2U8a91mu+v9dSZG3rnrZ0A1Peqj5dD1p+c8rpbt25Rt25drl+/zv/+9z9u3LjB0qVLtRccT58+NVpdgwYN4sqVKxw7dizdc6tWrcLDw4MOHTroVdbUqVN5+PAh//zzD++88w5Dhgxh165dRos1OyZPnsyAAQOMWma7du14+PAht27dYu7cuSxbtoxJkyYZtY78wuAk3atXL7755hsmTpxIzZo1OX/+PLt379beTLx79y4PHz7U7r9kyRKSk5Pp3r073t7e2q9vvvnGeGdhRiLdpP7G7dUhhGD06NHMnTsXgPr165v2Rl30Pbh9hFiFgmtl6nEt6pr2a9utbQB0LNvRdPXnkhEjRqBUKtm7dy/NmzendOnStG/fnv3793P//n3t5FSguYKdMWMG7777Lk5OTpQuXZrly5frlBcWFkbPnj1xdXXFzc2NLl26cPv2bQBq1qxJ7dq1WbFihc4xQghWrVpFYGAgVlZWJCcnM3LkSLy9vbG1taVMmTLMnDlT5xgnJye8vLwoW7YsY8eOxc3NjX379mmff/bsGYMHD6ZYsWI4OzvTqlUrLly4oH3+woULtGzZEicnJ5ydnalTpw6nT58mJCSEgQMHEh0drb2KnTx5spFebcPZ2Njg5eVFqVKl6Nq1K/7+/jrnWZhk68bhyJEjGTlyZIbPhYSE6DxO+0MtiEzdk0MIwUcffcR3330HwLJly4w7H3RG/tlMnAI6lSnD0+AR6Z5WWijxL5N5zxwhBAkppu1+mBFDpnR9+vQpe/bsYfr06dj9250wjZeXF3379mXdunUsXrxYW+a3337LtGnT+Oyzz9i4cSPvv/8+zZs3p2LFiqSkpBAQEEDDhg05fPgwVlZWfPnll7Rr146//voLpVLJoEGDGDduHPPnz8fBwQHQvFdCQ0N59913Ac1N4N9//53169dTunRpwsLCCAsLy/Ac1Go1W7ZsISoqSrskGkCPHj2ws7Nj165duLi4sGzZMlq3bs21a9dwc3Ojb9++1KpViyVLlmBpacn58+extramUaNGzJs3j4kTJ3L16lUAHB0dDfslmMg///zDsWPHKFOmcC7LJufuyIGsJvXPaa8OIQQffvghCxcuBDTD6wcPHpzt8vR2dRch9nY8VagBKGpbVPuUQqGgV8VeOCmdMj08IUVFlYl7TB7myy5NDdB7Ktjr168jhMi0nbhy5cpERUXx+PFj7SjaDh06MHz4cEDTjXTu3LkEBwdTsWJF1q1bh1qt5ocfftAm9ZUrV+Lq6kpISAht27alT58+fPzxx2zYsEHbNLBy5UqaNGminWPl7t27lC9fniZNmqBQKDJMSmPHjuXzzz8nKSmJ1NRU3NzctH8XR44c4dSpUzx69Eh74/2bb75h69atbNy4kaFDh3L37l3GjBlDpUqVAChfvry2bBcXFxQKxSs7AeSG7du34+joSGpqKklJSVhYWGjfC4WNTNJG8vKk/jmZrB80bf8LFy5EoVDw/fffM2jQIGOEmbWEKAg7yf5imt4cQ18byge1TNC9L48wpG3/xR5JaYksbcjvhQsXuHHjBk5Ouv+8EhMTuXnzJqCZw6Fbt26sWLGCAQMGEBMTw6ZNm7RTyoLmhlmbNm2oWLEi7dq1o1OnTumG+I8ZM4YBAwbw8OFDxowZw/Dhw7WTal24cIG4uDiKFi2qc0xCQoI2jqCgIAYPHszPP/+Mv78/PXr0wM/PT+/XAeDw4cO0b//fjePk5GSEEGzcuFG7bdmyZfTt29egcl/UsmVLlixZwvPnz5k7dy5WVla89dZb2S4vP5NJ2kiMPal/q1atWLJkCYsXL9Z+HDa5GweIR82Rf0dJtSnTxuAi7KwtuTQ1wNiR6VWvvsqVK4dCoeDy5cu8+eab6Z6/fPkyRYoUoVix/6Zetba21tlHoVCgVms+bcTFxVGnTh3WrFmTrqwXyxg0aBCtW7fmxo0bBAcHY2lpSY8ePbTP165dm9DQUHbt2sX+/fvp2bMn/v7+OsnP3d2dcuXKUa5cOTZs2ED16tWpW7cuVapUIS4uDm9v73RNjoC2N8XkyZPp06cPO3bsYNeuXUyaNIm1a9dm+Dpkpm7dupw/f177+LvvvuP+/fvMmjVLu+3lAW+GcnBw0P7zWbFiBTVq1ODHH3/MnYuVPKbQJ+mctKGacuh3586duXnzZu5OQnV9L4ftbElSQEnHklQsUtHgIhQKRa6uQJMdRYsWpU2bNixevJjRo0frtEuHh4ezZs0a+vfvr/cnodq1a7Nu3To8PDxwdnbOdL+WLVvi6+vLypUrCQ4Opnfv3tr26TTOzs706tWLXr160b17d9q1a8fTp08znEahVKlS9OrVi/Hjx/Pbb79Ru3ZtwsPDsbKywsfHJ9M40qaxHT16NG+//TYrV67kzTffRKlUart4ZsXOzk6bQEEzf3lMTIzONmOysLDgs88+IygoiD59+qS7j1DQ5e13k4nlxhBufanVaiZOnMi7775L2bJlAXIlQQu1mqW/9+NK7G14HskNN1cA2vi0KdDDvRcuXEijRo0ICAjgyy+/xNfXl4sXLzJmzBhKlCjB9OnT9S6rb9++fP3113Tp0oWpU6dSsmRJ7ty5w+bNm/n0008pWbIkoPkH9u677zJnzhyioqK0PXbSzJkzB29vb2rVqoWFhQUbNmzAy8sryz7Fo0aNolq1apw+fRp/f38aNmxI165dmT17NhUqVODBgwfs2LGDN998k6pVqzJmzBi6d++Or68v9+7d488//9Q2I/j4+BAXF8eBAweoUaMG9vb2JpuXRaVS6VyNg6ZHR2b3CXr06MGYMWNYtGgRn3zyiUliyqsK9YJ0xljNG3J+k1CtVjNs2DCmT59O69atSUhIePVBRnLh0loWR//FQXUMB+2U3P33Y307HxMNlMkjypcvz+nTpylbtiw9e/bEz8+PoUOH0rJlS44fP57lBGAvs7e359ChQ5QuXZpu3bpRuXJlBg0aRGJiYror6wEDBhAdHU3VqlVp0KCBznNOTk7Mnj2bunXrUq9ePW7fvs3OnTuxyGLdyCpVqtC2bVsmTpyIQqFg586dNGvWjIEDB1KhQgV69+7NnTt38PT0xNLSkidPntC/f38qVKhAz549ad++vXaenEaNGjFs2DB69epFsWLFmD17tgGvqGHi4uKoVauWzlfnzpkvYmxlZcXIkSOZPXs2z58/N1lceZFCmHxkRM7FxMTg4uJCdHR0lh8nDRWfnKrtiZDd1bwhZzcJ1Wo17733Hj/88IN2uPc777yTrbKy48t1HViXGEaDxBQCfNuDW1lKlajH696vv/LYxMREQkND8fX1zXIuFkkqrLJ6j+ib1wp1c8eLcns1b9Ak6CFDhrBixQosLCz4+eef6dOnT67Vn5L0nD3xd8FCwbu1P6BRvfT9oiVJMi+ZpM1ErVYzePBgVq5ciYWFBb/88gtvv/12rsZw9NxSnlkoKKoW1K9Z+O6aS1J+UCiTdFqPDlNPzJ+VL7/8kpUrV2JpacmaNWvo1auXaSuMDYfYcFLUqdx6fh+VEGy8puna1d6xLFbWsrlCkvKiQpek80qPjuHDh/Pbb78xduxYevbsadrKntyEhfVAqJhQrCi7HHW7fXWqPsC09UuSlG2FLkln1KPDFBPzZ0QIob3B6O7uzqlTp4yy5Psr3fsThIqnFhbsddB0qbIVAhc1NLD1pEqFrqaPQZKkbCl0SfpFaT06cjqEWx8qlYoBAwbQpEkT3nvvPYDcSdAAUXcA2F2pBaqEG1QtWpW1ndbmTt2SJOVIoe4nndajw9QJOjU1lf79+/PLL7/wwQcfcOfOHZPWl07UbQB2qJ8B0Klsp9ytX5KkbCtUV9K5sYr3y1JTU+nXrx9r167FysqKdevW5f6Ui8/ucNfKir+SIrFQWNDOt2APVJGkgqTQJGlz3DBMTU3lnXfeYd26dVhbW7Nhwwa6dOmSa/VrRd1mh6OmLbqhd0Pc7dxzPwZJkrKl0DR35MYq3i9KSUmhT58+2gS9ceNG8yTo1CSIecDBf+dg6FBWv2WaJEnKGwpNkn7R6c/92TCsoUnbords2cKGDRuwtrZm06ZNvPHGGyarK0vR94iyUHDFRrN6R6PijcwTRx6jz2Knac+/+NWkSRMzRi0VRoWmueNFxlzFOzM9evTgypUr1K5dm06dzHijLiqUU7aaVTrKuZaTTR0vaNeuHStXriQlJYUzZ84QGBiIQqHQmRd55cqVOquyv7hUlSTlhkKZpE0lOTmZlJQUHBwcUCgUTJw40dwhQdQdTtlpRhPqM2lSYZK22Clo5mZOW+z0xSTt6uqaJ5aTkgovmaSNJDk5mV69evHs2TN27Nhhsnl4XxYWG0ZUYiY3Q2Puw+1g1jtrlnWq71Xf9AEJASnxpq/nZdb2kINPR4V9sVMp75JJ2giSk5Pp2bMnv/32GzY2Npw/f55GjUzf9nvu0TkCdwUiePVssxYoqOtV1+QxkRIPM4qbvp6XffYAlA6v3u8F+ix2+vbbb+sMOvrll1/o2rWrMSKWJL3IJJ1DSUlJ9OjRg23btmFjY8Nvv/2WKwka4Pu/vkcgcLVxxcH6pQQV8xBUyaCwAEsr2lfonuUq34WRPoudzp07F39/f+1jb2/v3A5TKuRkks6BpKQkunfvzvbt27G1teW3335Lt7qzqdyIusHh+4dRoGBNhzWUdi7935OXfof1/UDpCKMugEMu3iy0ttdc1eY2a8Obl/RZ7NTLy8tka/dJkj5kks6mpKQk3nrrLXbs2IGtrS3btm3TueIytVUXVwHgX8ZfN0GrVRD87/p8r7+fuwkaNO3CBjY75AWFfbFTKe8qlP2kjeH27dscO3YMOzs7tm/fnqsJ+lH8I3aE7gBgQNUBuk/+vQEeXwFbV2g4MtdiKgh69OiBpaUlixYtMncokqQlk3Q2VaxYkf3797N9+3Zat26dq3WvubyGVHUqtT1q81qx1/57IjUZQmZqfm48CuxcczWu/K4wL3Yq5V2yucMAiYmJXLt2jdde0yTG2rVr53oMKeoUtt7YCkBg1UDdJ8+s1Mx45+ABDd7L9djyk1WrVmW4fdy4cYwbNw7QzPciSeYmr6T1lJCQQJcuXWjSpAnHjx83WxzHHxznaeJT3GzdaFay2QsBPoOQrzQ/txyfL9uFJUlKTyZpPaQl6L1796JWq0lJSTFbLNtvbQegg28HrCxe+CB0ZA4kPAX3ilCrv5mikyTJ2GRzxyvEx8fTpUsX9u/fj4ODA7t27aJp06ZmieV5ynOC7wYD0LFsx/+eiLoDJ5Zqfm47DSzlr1WSCooC/27Oycrg8fHxdO7cmYMHD+Lo6MiuXbvMMgtakiqJy08uc/LhSRJVifg4+1C1aNX/djg4DVRJ4NsMyudOP21JknJHgU7SOZnoPz4+nk6dOhEcHIyjoyO7d++mcePGJojy1b448gW7bu/SPu5YtuN/s/hFXtd0u0MBbb/M0fwVkiTlPQU6SedkZXBLS0vs7OxwcnJi9+7duTbU+2VRiVHsu7MPgDLOZShqW5TuFbr/t8N1zXOUbQHeNXI/QEmSTKpAJ+kXGboyuI2NDZs2bdLpcmcOe2/vJVWkUtmtMus7r0+/Q+gfmu9+LXM3MEmSckWh6d2hz8rgcXFxLF68WNs/1tbW1qwJGv7rzZHhCt+qVLh9VPOzb/NcjEqSpNxSaK6kXyU2NpYOHTpw5MgRHj58yLRp08wdEmGxYZx/fB4LhQXtfdun3+HBOUiO1QwB9zLvPxNJkkxDJmk0Cbp9+/YcPXoUFxcXo69HmKpOZdapWdyMvmnQcU8SngDQwKsBxeyLpd8hNETz3bcpWBSaD0WSVKgU+nd2TEwM7dq14+jRo7i6urJ//37q1atn1Dq239rO2qtr+TP8T4O+bkXfAuDN8m9mXLBs6siRsLAw3n33XYoXL45SqaRMmTKMGjWKJ0+eGLWeDz74gMqVK2f43N27d7G0tOT3339/ZTk+Pj7aBXHt7e2pXr06P/zwg9HiXLVqFa6urkYrL7v16LNIsCnoW29uL1BcqK+k0xL08ePHKVKkCPv27aNOnTpGrSNZlcyS80sA6FWxF3U9DVsdxdnGmYbeDdM/oVbBvdOan0vLtQsNdevWLRo2bEiFChX43//+h6+vLxcvXmTMmDHs2rWLEydO4ObmZpS6Bg0axMKFCzl27Fi6XkKrVq3Cw8ODDh066FXW1KlTGTJkCPHx8WzYsIEhQ4ZQokQJ2rfPoDksl0yePJnbt29nOh9KduizSPCrtGjRggEDBjBgwACj15urCxSLfCA6OloAIjo62qDjnieliDJjt4syY7eL50kpOs+lpqaKxo0bC0AUKVJEnDlzxpgha/16+VdRbVU10XJdS5GQkmC8gh/+JcQkZyGmlxBClWq8cg2QkJAgLl26JBISjHheuaRdu3aiZMmSIj4+Xmf7w4cPhb29vRg2bJh2W5kyZcT06dPFwIEDhaOjoyhVqpRYtmyZznF3794VPXr0EC4uLqJIkSLijTfeEKGhodrna9euLQYNGqRzjFqtFr6+vmLs2LFCCCGSkpLEiBEjhJeXl7CxsRGlS5cWM2bM0Ilj7ty5OmW4ubmJ0aNHax9HRUWJQYMGCXd3d+Hk5CRatmwpzp8/r33+/PnzokWLFsLR0VE4OTmJ2rVriz///FMEBwcLQOdr0qRJer2WkyZNEoGBgXrtK4QQK1euFC4uLpk+HxgYKLp06aKzrVu3bqJWrVp61yGEEM2bNxcrV67Ue3996wXEli1b9Cozq/eIvnmt0DZ3WFpa8t577+Hu7s6BAwdMMqNdfEo8yy4sA+C9197D1srWeIWHndR8L1kXLF7d7zu3CCGIT4nP9S9hwIx1T58+Zc+ePQwfPjzd5P5eXl707duXdevW6ZT57bffUrduXc6dO8fw4cN5//33uXr1KgApKSkEBATg5OTE4cOHOXr0KI6OjrRr147k5GRAczW9fv16nSlQQ0JCCA0N5d133wXgu+++4/fff2f9+vVcvXqVNWvW4OPjk+E5qNVqNm3aRFRUlM5VXI8ePXj06BG7du3izJkz1K5dm9atW/P06VMA+vbtS8mSJfnzzz85c+YM48aNw9ramkaNGjFv3jycnZ15+PAhDx8+5JNPPtH7NTWltEWCTXq1mofqfVmBbu541fu2X79+vPHGG7i4uOhVXmRCJLee3dK7/j/u/cGTxCeUcCxBt/Ld9D5OL2GnNN9LNTBuuTmUkJpAg19zP6aTfU5ir+cSWtevX0cIkWk7ceXKlYmKiuLx48d4eHgA0KFDB4YPHw7A2LFjmTt3LsHBwVSsWJF169ahVqv54YcftF08V65ciaurKyEhIbRt25Y+ffrw8ccfs2HDBu3H75UrV9KkSRMqVKgAaNqny5cvT5MmTVAoFBmuXD527Fg+//xzkpKSSE1Nxc3NjcGDBwNw5MgRTp06xaNHj7CxsQHgm2++YevWrWzcuJGhQ4dy9+5dxowZQ6VKlQAoX768tmwXFxcUCgVeXl56vY6mpM8iweasNzcXKC6wSVoIQY+lulOKRkVFMXLkSL755hvtgqL6JuiE1AR6bOtBZEKkwbEMrzkca0trg4/LUtqVdOm8laTzE0Ouvl/sL5+WyB49egTAhQsXuHHjBk5Ougv9JiYmcvOmpkePq6sr3bp1Y8WKFQwYMICYmBg2bdqkswrMgAEDaNOmDRUrVqRdu3Z06tQp3ZqZY8aMYcCAATx8+JAxY8YwfPhw7RqMFy5cIC4ujqJFi+ock5CQoI0jKCiIwYMH8/PPP+Pv70+PHj3w8/PT+3UAOHz4sE4beHJyMkIINm7cqN22bNky+vbta1C5L9JnkeCXzZgxgxkzZmgfJyQkcOLECUaO/G+FokuXLlG6dOmMDjeo3txcoLjAJumEFBWXHsYAUMXbmcS4GNq2bcuZM2cICwvjjz/+0GvkYZqdt3YSmRCJvZU9xR2L631cZbfKdPTt+OodDZEQpZncH6B4LeOWnUN2Vnac7HPSLPXqq1y5cigUCi5fvsybb6bvOXP58mWKFClCsWL/dXu0ttb9J6tQKFCr1YBmEFSdOnVYs2ZNurJeLGPQoEG0bt2aGzduEBwcjKWlJT169NA+X7t2bUJDQ9m1axf79++nZ8+e+Pv76yQ/d3d3ypUrR7ly5diwYQPVq1enbt26VKlShbi4OLy9vQkJCUkXR1pvismTJ9OnTx927NjBrl27mDRpEmvXrs3wdchM3bp1OX/+vPbxd999x/3793Vurnl6eupdXkb0WST4ZcOGDaNnz57ax3379uWtt96iW7f/PsUWL571e1ffenN1gWK9Wr9fsnDhQlGmTBlhY2Mj6tevL06ePJnl/uvXrxcVK1YUNjY2olq1amLHjh0G1ZedG4cv3jQMe/hI1K5dWwDC3d1d/PXXXwbVr1arRbffuolqq6qJVf+sMuhYk7gZorlpOO81s4aRn28ctm3bVpQoUULvG4cv37CrUaOG9sba8uXLRZEiRV7595l2o/Czzz4TDRs2FEOGDMly/927dwtAPHnyJNM4hg4dKt544w0hhBB79+4VlpaWOjcsX6V3796ic+fOQggh1qxZIxwdHfU+Nk1u3Dj89ddfhZeXV7rfV1aMceMwo3rJ6zcO161bR1BQEJMmTeLs2bPUqFGDgIAA7Ue/lx07doy3336bQYMGce7cObp27UrXrl35559/cvK/xSCdO3fi7NmzFCtWjODgYKpXr27Q8WcfneVa1DVsLW3pWq6raYI0xMMLmu9yQqVsW7hwIUlJSQQEBHDo0CHCwsLYvXs3bdq0oUSJEkyfPl3vsvr27Yu7uztdunTh8OHDhIaGEhISwocffsi9e/e0+ykUCt59912WLFnC8ePH012dzZkzh//9739cuXKFa9eusWHDBry8vLLsUzxq1Ci2bdvG6dOn8ff3p2HDhnTt2pW9e/dqF0ueMGECp0+fJiEhgZEjRxISEsKdO3c4evQof/75p7Zt3sfHh7i4OA4cOEBkZCTx8fGGvagGUKlUnD9/Xufr8uXLme5vrkWC88LixAYn6Tlz5jBkyBAGDhxIlSpVWLp0Kfb29qxYsSLD/efPn0+7du0YM2YMlStXZtq0adSuXTtXbgKkuXDhAh4eHgQHB1OtWjWDj//18q+AZopQFxv92rBNSpuka5o1jPysfPnynD59mrJly9KzZ0/8/PwYOnQoLVu25Pjx4wb1kba3t+fQoUOULl2abt26UblyZQYNGkRiYiLOzs46+w4YMIDo6GiqVq1Kgwa69xOcnJyYPXs2devWpV69ety+fZudO3dikcVo0ipVqtC2bVsmTpyIQqFg586dNGvWjIEDB1KhQgV69+7NnTt38PT0xNLSkidPntC/f38qVKhAz549ad++PVOmTAGgUaNGDBs2jF69elGsWDFmz55twCtqmLi4OGrVqqXz1blz50z3N9ciwXlhcWLFv5fveklOTsbe3p6NGzfq3MkMDAzk2bNn/Pbbb+mOKV26NEFBQXz00UfabZMmTWLr1q1cuHAhw3qSkpJISkrSPo6JiaFUqVJER0en+6PPzF+XtzHpjwmaBymJ2Nvb69yNNcRfJKNSwMbUYlTEyDcAsyPshOb7O5uhXO6uVP6ixMREQkND8fX1xdbWiN0LJamAyOo9EhMTg4uLyyvzmkE3DiMjI1GpVOluCnh6enLlypUMjwkPD89w//Dw8EzrmTlzpva/e3YlJD7lht2//3/sbADVv1/Z0yAhkYrhZ3IUk1FZ2+e5m4aSJBlfnuzdMX78eIKCgrSP066kDVGhTFNmx95DANYWChRkf8USC4WCOk6+YEAPApMrVgnsjTNsWZKkvMugJO3u7o6lpSURERE62yMiIjLtAO/l5WXQ/qCZcD+tM352FXErS/tmE3JUhiRJkrkZdONQqVRSp04dDhw4oN2mVqs5cOAADRtmMAkQ0LBhQ539Afbt25fp/pIkSdJ/DG7uCAoKIjAwkLp161K/fn3mzZvH8+fPGThwIAD9+/enRIkSzJw5E9B0EWrevDnffvstHTt2ZO3atZw+fZrly5cb90wkSZIKIIOTdK9evXj8+DETJ04kPDycmjVrsnv3bu3Nwbt37+p0GWrUqBG//vorn3/+OZ999hnly5dn69at2eoKJ+VNaSPvJEnSZYz3hkFd8MxF364qUu5Sq9Vcv34dS0tLihUrhlKpNGiovSQVVEIIkpOTefz4MSqVivLly6fr726SLniS9CILCwt8fX15+PAhDx48MHc4kpTn2NvbU7p06SwHJL2KTNJSjiiVSkqXLk1qaioqVfb7oUtSQWNpaYmVlVWOP13KJC3lWNqacC/PFCdJUs4V2pVZJEmS8gOZpCVJkvIwmaQlSZLysHzRJp3WSzAmJsbMkUiSJBlHWj57VS/ofJGkY2NjAQyeZEmSJCmvi42NzXKt1XwxmEWtVvPgwQOcnJwM6s6SNnteWFhYgRwEU9DPDwr+Ocrzy/+ye45CCGJjYylevHiW/ajzxZW0hYUFJUuWzPbxzs7OBfYPBAr++UHBP0d5fvlfds4xqyvoNPLGoSRJUh4mk7QkSVIeVqCTtI2NDZMmTcrxAgJ5VUE/Pyj45yjPL/8z9TnmixuHkiRJhVWBvpKWJEnK72SSliRJysNkkpYkScrDZJKWJEnKw/J9kl60aBE+Pj7Y2trSoEEDTp06leX+GzZsoFKlStja2lK9enV27tyZS5FmjyHn9/3339O0aVOKFClCkSJF8Pf3f+XrYW6G/v7SrF27FoVCQdeuXU0boBEYeo7Pnj1jxIgReHt7Y2NjQ4UKFfL036mh5zdv3jwqVqyInZ0dpUqVYvTo0SQmJuZStIY5dOgQnTt3pnjx4igUCrZu3frKY0JCQqhduzY2NjaUK1eOVatW5SwIkY+tXbtWKJVKsWLFCnHx4kUxZMgQ4erqKiIiIjLc/+jRo8LS0lLMnj1bXLp0SXz++efC2tpa/P3337kcuX4MPb8+ffqIRYsWiXPnzonLly+LAQMGCBcXF3Hv3r1cjlw/hp5fmtDQUFGiRAnRtGlT0aVLl9wJNpsMPcekpCRRt25d0aFDB3HkyBERGhoqQkJCxPnz53M5cv0Yen5r1qwRNjY2Ys2aNSI0NFTs2bNHeHt7i9GjR+dy5PrZuXOnmDBhgti8ebMAxJYtW7Lc/9atW8Le3l4EBQWJS5cuiQULFghLS0uxe/fubMeQr5N0/fr1xYgRI7SPVSqVKF68uJg5c2aG+/fs2VN07NhRZ1uDBg3Ee++9Z9I4s8vQ83tZamqqcHJyEqtXrzZViDmSnfNLTU0VjRo1Ej/88IMIDAzM80na0HNcsmSJKFu2rEhOTs6tEHPE0PMbMWKEaNWqlc62oKAg0bhxY5PGaQz6JOlPP/1UVK1aVWdbr169REBAQLbrzbfNHcnJyZw5cwZ/f3/tNgsLC/z9/Tl+/HiGxxw/flxnf4CAgIBM9zen7Jzfy+Lj40lJScHNzc1UYWZbds9v6tSpeHh4MGjQoNwIM0eyc46///47DRs2ZMSIEXh6elKtWjVmzJiRJ9ePzM75NWrUiDNnzmibRG7dusXOnTvp0KFDrsRsaqbIMfligqWMREZGolKp8PT01Nnu6enJlStXMjwmPDw8w/3Dw8NNFmd2Zef8XjZ27FiKFy+e7o8mL8jO+R05coQff/yR8+fP50KEOZedc7x16xYHDx6kb9++7Ny5kxs3bjB8+HBSUlKYNGlSboStt+ycX58+fYiMjKRJkyYIIUhNTWXYsGF89tlnuRGyyWWWY2JiYkhISMDOzs7gMvPtlbSUta+++oq1a9eyZcsWbG1tzR1OjsXGxtKvXz++//573N3dzR2OyajVajw8PFi+fDl16tShV69eTJgwgaVLl5o7NKMICQlhxowZLF68mLNnz7J582Z27NjBtGnTzB1anpVvr6Td3d2xtLQkIiJCZ3tERAReXl4ZHuPl5WXQ/uaUnfNL88033/DVV1+xf/9+XnvtNVOGmW2Gnt/Nmze5ffs2nTt31m5Tq9UAWFlZcfXqVfz8/EwbtIGy8zv09vbG2toaS0tL7bbKlSsTHh5OcnIySqXSpDEbIjvn98UXX9CvXz8GDx4MQPXq1Xn+/DlDhw5lwoQJWc6rnB9klmOcnZ2zdRUN+fhKWqlUUqdOHQ4cOKDdplarOXDgAA0bNszwmIYNG+rsD7Bv375M9zen7JwfwOzZs5k2bRq7d++mbt26uRFqthh6fpUqVeLvv//m/Pnz2q833niDli1bcv78+Ty5ak92foeNGzfmxo0b2n9AANeuXcPb2ztPJWjI3vnFx8enS8Rp/5BEAZhGyCQ5Jtu3HPOAtWvXChsbG7Fq1Spx6dIlMXToUOHq6irCw8OFEEL069dPjBs3Trv/0aNHhZWVlfjmm2/E5cuXxaRJk/J8FzxDzu+rr74SSqVSbNy4UTx8+FD7FRsba65TyJKh5/ey/NC7w9BzvHv3rnBychIjR44UV69eFdu3bxceHh7iyy+/NNcpZMnQ85s0aZJwcnIS//vf/8StW7fE3r17hZ+fn+jZs6e5TiFLsbGx4ty5c+LcuXMCEHPmzBHnzp0Td+7cEUIIMW7cONGvXz/t/mld8MaMGSMuX74sFi1aVLi74AkhxIIFC0Tp0qWFUqkU9evXFydOnNA+17x5cxEYGKiz//r160WFChWEUqkUVatWFTt27MjliA1jyPmVKVNGAOm+Jk2alPuB68nQ39+L8kOSFsLwczx27Jho0KCBsLGxEWXLlhXTp08XqampuRy1/gw5v5SUFDF58mTh5+cnbG1tRalSpcTw4cNFVFRU7geuh+Dg4AzfU2nnFBgYKJo3b57umJo1awqlUinKli0rVq5cmaMY5FSlkiRJeVi+bZOWJEkqDGSSliRJysNkkpYkScrDZJKWJEnKw2SSliRJysNkkpYkScrDZJKWJEnKw2SSliRJysNkkpYkScrDZJKWJEnKw2SSliRJysNkkpYkScrD/g/3hesp4Vk96wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1283,7 +2417,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.5556820682740744, 0.7318154142418192, 0.7349919877544187)"
+       "(0.5863418891045122, 0.744300691021032, 0.7371402896099202)"
       ]
      },
      "execution_count": 15,
@@ -1326,7 +2460,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1364,7 +2498,8 @@
       "text/html": [
        "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1376,20 +2511,21 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-3.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
        "#sk-container-id-3 {\n",
@@ -1509,12 +2645,21 @@
        "/* Toggleable label */\n",
        "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
@@ -1532,9 +2677,7 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -1560,9 +2703,9 @@
        "\n",
        "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
        "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -1616,7 +2759,6 @@
        "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
@@ -1662,23 +2804,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -1688,7 +2832,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1698,7 +2843,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -1738,7 +2884,7 @@
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -1750,6 +2896,7 @@
        "\n",
        "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
@@ -1766,7 +2913,569 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "            element.innerHTML = \"Failed!\";\n",
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+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
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+       "    element.setAttribute('title', fullParamName);\n",
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+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
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+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
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+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
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+       "\n",
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+       "        return 'light';\n",
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+       "\n",
+       "    // Guess based on a parent element's color\n",
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+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
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+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
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+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-3');</script></body>"
       ],
       "text/plain": [
        "RandomForestClassifier()"
@@ -1799,9 +3508,9 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.42, 0.58],\n",
-       "       [0.89, 0.11],\n",
-       "       [0.64, 0.36]])"
+       "array([[0.45, 0.55],\n",
+       "       [0.6 , 0.4 ],\n",
+       "       [0.58, 0.42]])"
       ]
      },
      "execution_count": 18,
@@ -1831,7 +3540,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.5556820682740744, 0.7731918190932655)"
+       "(0.5863418891045122, 0.7804311144471703)"
       ]
      },
      "execution_count": 20,
@@ -1854,7 +3563,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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rJzExMZlYZkY/6fns9+zZE7Vaze+//87ixYsxMzOje/fuevulke29O5LYvHmz1rd44MCBYsmSJWL+/PmiY8eOwsLCQvTq1UtbN8kFr23btmL+/Pna/bdd8BYtWqT1cFi4cKHo06eP8PLyEs7Ozil6d7zt/iZEctet+fPna123OnTooFN30qRJWpezqVOnioULF4rvvvtOFCtWTEybNu2d1/8uG9atW5fMDS8xMVG0bt1aAKJOnTpi9uzZYvHixaJz587CxMRElC5dWgQFBem0o1KpRKdOnQQgKlWqJCZOnCiWLl0qJk6cKKpWrSoA8e+//77TzvQwevRo7evxyy+/iLlz54rOnTuLESNGaOskvb6NGzcW8+bNE19//XWqLngpeT0k+Sq/DSD69++frJ/33cfr168LCwsLUbZsWTFv3jwxefJk4e3tLcqXLy8Ace/evff2nZ423vbuqFSpkmjWrJn4+eefxW+//Sa+/fZboVQqRfPmzbV1klzw3N3dxc8//yymTJkiihQpkqoL3tvvqST3zCR3wdTYsWOHjmvcqFGjUnWN6927twBE06ZNxcyZM8W8efPEwIEDRYECBbS+7JnZT1o/+0uXLhWAWL58ubZs5cqVAhDz589/p91pJceItBBC3Lx5U/Ts2VN4enoKCwsLYWdnJ2rWrCnmzp2rs1AlISFBjB8/Xnh5eQlzc3Ph4eGR4mIWlUolhg8frnXi9/f3F7dv307VBe9dIn316lXRpk0bYWdnJ/LkySMGDBiQ4mKWDRs2iFq1agkbGxthY2MjSpYsKfr37y9u3Ljxzmt/lw0qlUp4e3sLb29vHRdBlUolli1bJmrWrCns7e2FpaWlKF26tBg/fvw7nfDXr18vGjduLJycnISZmZlwc3MT7dq1EwEBAe+08UNYunSpqFixolAqlSJPnjyibt26yRaazJs3T5QsWVKYm5uLfPnyib59+6a6mOVt0ivSabmP//zzjyhXrpywtLQUnp6eYsqUKdoPc1pEOj1tvC3Sv/76q6hTp47ImzevUCqVwtvbWwwbNkyEh4frtH/27Fnh7+8vbG1thbW1tahfv36yL9iMirQQmvdzqVKlhFKpFD4+PqkuMhFC4+Pt6+srrKyshJ2dnShbtqz47rvvUlxFbOh+0vLZDwwMFA4ODjpfgEm0atVK2NjYiLt3777X9vehEEJfY3KJJOcybtw4xo8fz7Nnz3B2dja2OZJcRI6Yk5ZIJJKcihRpiUQiycJIkZZIJJIsjJyTlkgkkiyMHElLJBJJFkaKdBbmyJEjTJgwIUPxMiQSSfZGinQW4P79+ygUCpYvX65T7uvry86dO+nRo0eq5wYEBKBQKAgICDCskZIsTVZ9H2RVu7ITUqSzMFZWVmzZsoXz589rlx9L0seTJ08YN24c58+fN3hfMTExjBs3TgqSRK9Ikc7i5M2blx07dhAVFUVCQoKxzcl2PHnyhPHjx2eaSI8fP16KtESvSJHOBhQpUoSRI0emOaZ0biM6OtrYJkgkBkOKNJolvwqFgps3b/Lll1/i4OCAi4sLo0ePRghBYGAgLVq0wN7envz58zN9+vRkbYSEhNC9e3fy5cuHpaUl5cuXZ8WKFcnqhYWF0bVrVxwcHHB0dKRLly6pRvm6fv06bdq0wcnJCUtLS3x9fdm0aVOarunEiRM0adIEBwcHrK2tqVu3LkePHtWpExkZyaBBg/D09ESpVOLq6kqjRo10AqW/6/W6fv06bdu2xd7enrx58zJw4EBtpL0kEhMT+fHHH/H29kapVOLp6cn333+vjb2bhEKhSDG2tKenJ127dtXuL1++HIVCwcGDB+nXrx+urq46WWfeJCAggCpVqgDQrVs3FApFsrn/devW4evri5WVFc7Oznz55Zc8fvxYp5169epRr169ZO137dpVG6L0/v372njE48eP1/b15jW9fT8rV67MP//8o9NmQkIC48ePp1ixYlhaWpI3b15q1arFnj17UrzG92HI9wHA48eP6d69OwUKFECpVOLl5UXfvn2Jj49P9ZzDhw/z+eefU6hQIZRKJR4eHgwePFgnjC1AUFAQ3bp1o2DBgiiVStzc3GjRooVO9L7Tp0/j7++Ps7MzVlZWeHl58dVXX+m0o1armTVrFqVLl8bS0pJ8+fLRu3fvZEkL0tKWMcgR8aT1Rbt27ShVqhSTJ09m27Zt/PTTTzg5OfHrr7/SoEEDpkyZwqpVqxg6dChVqlShTp06ALx69Yp69epx+/ZtBgwYgJeXF+vWraNr166EhYUxcOBAQBN6skWLFhw5coQ+ffpQqlQpNm7cSJcuXZLZcuXKFWrWrImbmxvDhw/H1taWtWvX8tlnn7F27VratGmT6nXs37+fpk2b4uvry9ixYzExMWHZsmU0aNCAw4cPU7VqVQD69OnD+vXrGTBgAD4+Prx48YIjR45w7do1KlWq9N7Xq23btnh6ejJp0iSOHz/OnDlzePnypTYWNUCPHj1YsWIFbdq04dtvv+XEiRNMmjSJa9eusXHjxnTdnzfp168fLi4ujBkzJtWRdKlSpZgwYQJjxoyhV69e1K5dG4AaNWoAGsHv1q0bVapUYdKkSQQHBzN79myOHj3KuXPncHR0TLM9Li4uLFy4kL59+9KqVSs+++wzAMqVKwe8vp/u7u6MGDECGxsb1q5dS8uWLdmwYQOtWrUCNF+AkyZNokePHlStWpWIiAhOnz7N2bNnadSoUbpeI0O/D548eULVqlUJCwujV69elCxZksePH7N+/XpiYmKwsLBI8bx169YRExND3759yZs3LydPnmTu3Lk8evSIdevWaeu1bt2aK1eu8PXXX+Pp6UlISAh79uzh4cOH2v3GjRvj4uLCiBEjcHR05P79+/z99986/fXu3Vt7r7/55hvu3bvHvHnzOHfuHEePHsXc3DzNbRmFDIdoygEkRTh7M5xpYmKiKFiwoFAoFNrM30II8fLlS2FlZaUTBS8psevKlSu1ZfHx8aJ69erC1tZWm3V606ZNAhBTp07V6ad27drJEp82bNhQ+Pj46CQkVavV4qOPPhLe3t7asrcjkqnValGsWDHh7++vkzw0JiZGeHl5iUaNGmnLHBwcdCK9pff1el9S1sxO+psSp06dSvbaCqG5P66urqJMmTI6Uey2bt0qADFmzBht2duR5pJ4O8ras2fPUr2Ohg0birJly+pEWlSr1aJGjRqiWLFi2rLy5cunGhnvXRjjfZAU1jalyItJfaYUMS+lJLuTJk0SCoVCPHjwQAih+ZwB7wzRu3HjxlQjPyaR1uSzaWnLWMjpjjd409XN1NSUypUrI4Sge/fu2nJHR0dKlCihk6R0+/bt5M+fn/bt22vLzM3N+eabb4iKiuLgwYPaemZmZvTt21enn6RkpkmEhoayf/9+unTpgkKhIDY2ltjYWOLi4mjZsiV37tzh0aNHKV7D+fPnuXXrFh06dODFixfaZJvR0dE0bNiQQ4cOaYPoOzo6cuLECZ48efJBr9f7krIaK+lvWjh9+jQhISH069cPS0tLbfnHH39MyZIlM2Tb2yTdz7Zt2xIZGam9Jy9evMDf359bt25pp1gcHR25cuVKiplG0oOh3wdqtZpNmzbRvHlzbbq2N3lXero3s6JER0fz/PlzatSogRCCc+fOaetYWFgQEBCQai7FpF86W7duTfWhelqTz6alLWMhRfoN3k646eDggKWlZbLQlA4ODjpvnAcPHlCsWLFkWa2T0nMl5fV78OABbm5uyfIVvp0U9Pbt2wghGD58OFZWVjp/SSmwnj17luI1JH24u3TpgouLi87fb7/9RlxcnDaDxdSpU7l8+TIeHh5UrVqVcePGpZghOzXel5TVWEl/00JqSWxBk88xI7a9TdL9HD16dLJ7MnbsWOB1EtQJEyYQFhZG8eLFKVu2LMOGDdPmm0wPhn4fPHv2jIiICMqUKZNu2x4+fEjXrl1xcnLC1tYWFxcX6tatC6C1SalUMmXKFHbs2EG+fPmoU6cOU6dOJSgoSNtO3bp1ad26NePHj8fZ2ZkWLVqwbNkynecdbyaffft1iIqK0r7uaWnLWMg56TdIaWRm6CScKZE0wvnhhx+0OQbfJrU8iEnnTps2LdXcgElfEm3btqV27dps3LiR3bt3M23aNKZMmcLff/+tTQeVHlIbPRk76W9GUSgUKd7vtKZRS7onSbnvUiLpi6xOnTrcuXOHzZs3s3v3bn777TdmzpzJokWL3rmoKbU+jfE+eBcqlYpGjRoRGhrK8OHDKVmyJDY2Njx+/JiuXbvqpEobNGgQzZs3Z9OmTezatYvRo0czadIk9u/fT8WKFVEoFKxfv57jx4+zZcsWdu3axVdffcX06dM5fvw4tra2aU4+m5a2jIUUaT1QuHBhLl68iFqt1hlNX79+XXs86f++ffuIiorSuelvJ8xMyv2XmJiozTGYVry9vQFNFvW0JGJ1c3OjX79+9OvXj5CQECpVqsTPP/+cpg/n+5KyGivp75uk9gXxZhLbBg0a6By7ceNGMttSGlm+PdpOra+k+2lubp6me+Lk5ES3bt3o1q0bUVFR1KlTh3HjxqVLpA39PnBxccHe3p7Lly+n2SbQZNK+efMmK1asoHPnztry1LxXvL29+fbbb/n222+5desWFSpUYPr06axcuVJb56OPPuKjjz7i559/5s8//6Rjx46sXr2aHj16pDvJ7bvaMhZyukMPNGvWjKCgINasWaMtS0xMZO7cudja2mp/yjVr1ozExEQWLlyoradSqZKtJnR1daVevXosXrw4mTsYoPOT7218fX3x9vbml19+ISoqKtnxpGkSlUqlk7gzqd8CBQqk+Sfe+5KyGivp75vY2NgAJPsCqFy5Mq6urixatEjnenfs2MG1a9eS2Xb9+nWdKaYLFy4kc2WztrZOsa+k+/nrr7+m+KXzZrtvJ5K1tbWlaNGi6f7Zbej3gYmJCS1btmTLli2cPn062fHUfmmmlGRXCKFNFJ1ETExMMndOb29v7OzstHa9fPkyWT9JvxqS6qQ1+Wxa2jIWciStB3r16sWvv/5K165dOXPmDJ6enqxfv56jR48ya9Ys7OzsAGjevDk1a9ZkxIgR3L9/Hx8fH/7+++9kHxLQCGCtWrUoV64cPXv2xNvbm6dPn3L06FGePn2a6jyliYkJv/32G02bNqV06dJ069YNd3d3Hj9+zIEDB7C3t2fLli1ERkZSsGBB2rRpQ/ny5bG1tWXv3r2cOnUqRT/wlLh37x6ffvopTZo04dixY6xcuZIOHTpQvnx5AMqXL0+XLl1YvHgxYWFh1K1bl5MnT7JixQpatmxJ/fr1tW316NGDPn360Lp1axo1asSFCxfYtWtXhlNVeXt74+joyKJFi7Czs8PGxoZq1arh5eXFlClT6NatG3Xr1qV9+/ZaFzxPT08GDx6sbeOrr75ixowZ+Pv70717d0JCQli0aBGlS5cmIiJCW8/KygofHx/WrFlD8eLFcXJyokyZMpQpU0Z7P8uWLUvPnj0pUqQIwcHBHDt2jEePHnHhwgUAfHx8qFevHr6+vjg5OXH69Gmte1x6yIz3wcSJE9m9ezd169alV69elCpViqdPn7Ju3TqOHDmSogtjyZIl8fb2ZujQoTx+/Bh7e3s2bNiQ7OHgzZs3adiwIW3btsXHxwczMzM2btxIcHAwX3zxBQArVqxgwYIFtGrVCm9vbyIjI1myZAn29vbaAULdunXp3bs3kyZN4vz58zRu3Bhzc3Nu3brFunXrmD17Nm3atElTW0bDSF4lWYokl7Jnz57plHfp0kXY2Ngkq59SUtPg4GDRrVs34ezsrM30/LbblxBCvHjxQnTq1EnY29sLBwcH0alTJ3Hu3LkU3cTu3LkjOnfuLPLnzy/Mzc2Fu7u7+OSTT8T69eu1dVJLCnru3Dnx2WefaROSFi5cWLRt21bs27dPCCFEXFycGDZsmChfvryws7MTNjY2onz58mLBggVpfr3SkpQ1M5P+psbmzZuFj4+PMDMzS/Y6r1mzRpvo1snJSXTs2FE8evQoWRsrV64URYoUERYWFqJChQpi165dKSY6/ffff4Wvr6+wsLBI5o6Xlvv5008/iapVqwpHR0dhZWUlSpYsKX7++WedzOcpYYz3gRBCPHjwQHTu3Fm4uLgIpVIpihQpIvr37y/i4uJStevq1avCz89P2NraCmdnZ9GzZ09x4cIFnXvz/Plz0b9/f1GyZElhY2MjHBwcRLVq1cTatWu17Zw9e1a0b99eFCpUSCiVSuHq6io++eQTcfr06WR2vi/5bHraymxk0H9JupFJWSWSzEPOSUskEkkWRoq0RCKRZGGkSEskEkkWRs5JSyQSSRZGjqQlEokkCyNFWiKRSLIw2WIxi1qt5smTJ9jZ2WUoDoREIpFkFYQQREZGUqBAgWTB2d4kW4j0kydP8PDwMLYZEolEoncCAwNTzS4E2USkk5ZVBwYGYm9vb2RrJBKJJONERETg4eGh1bfUyBYinTTFYW9vL0VaIpHkKN43hSsfHEokEkkWRoq0RCKRZGGkSEskEkkWRoq0RCKRZGGkSEskEkkWRoq0RCKRZGGkSEskEkkWJt0ifejQIZo3b06BAgVQKBRs2rTpvecEBARQqVIllEolRYsWZfny5R9gqkQikeQ+0i3S0dHRlC9fPlmm6NS4d+8eH3/8MfXr1+f8+fMMGjSIHj16sGvXrnQbK5FIJLmNdK84bNq0KU2bNk1z/UWLFuHl5aXNPFyqVCmOHDnCzJkz8ff3T2/3EolEkqsw+LLwY8eO4efnp1Pm7+/PoEGDUj0nLi6OuLg47X5ERIShzJNIJLkAtVATHhfOzZc3OfrkKJeeXUIlVGk7OTEOIoMg8RWodc9JVKmJV6nxNivElK+2GMDyTBDpoKAg8uXLp1OWL18+IiIiePXqFVZWVsnOmTRpEuPHjze0aRKJJAegFmrC4sJ4FvOMmy9vcvn5Za6FXiMqIQohBOFx4byMfUmiSMx4Z2+H2TDT/FnFBGW87VTIkgGWRo4cyZAhQ7T7SdGiJBJJziUmIYa74Xe5E3aHe+H3iFO9/jUtEMQkxBCVEEV0QrTmf3w0EfERhMaGpnlU7GrtykduH1ElfxVszW1TryjU8Owm3NkHD49pyko0g7xFiYlX8b/jD3Sq1y1XKd3Xm1YMLtL58+cnODhYpyw4OBh7e/sUR9EASqUSpVJpaNMkEkkmoBZqnsU8IzAyUPsXHBNMeFw44XHhRMRHEB4XzovYFxnqx0HpgLeDNz55fSjtXJq8lnkBsFfak9cyL3kt82Juav7uRoKvwLEFcGsXRD97Xd5iAVTsCIDniG3Aa1Ge2KosHaoVypDt78LgIl29enW2b9+uU7Znzx6qV69u6K4lEkkmE6+KZ+vdrfxx9Q/uhd8DNCItSFu+67yWefF29MbLwQs7i9dxlhUosDa3xsbcBltzW+1/Wwtb8lrmxcnKCXOT9wjw+zj/J2wdDImxmn2lA5T8GMp/AUXqArBi18nX9dUqzo/2w9HOOmP9vod0i3RUVBS3b9/W7t+7d4/z58/j5OREoUKFGDlyJI8fP+Z///sfAH369GHevHl89913fPXVV+zfv5+1a9eybds2/V2FRCLJdOJV8QRHB/Mi9gUvXr3gZthN1t5Yy/NXz5PVNVWYkt8mPx52HnjYeVDAtgB5lHlwUDpgb2GPvdKe/Nb5cbR0zPwLAdj1Axybp9n2bgg1v4FCNcDMQlvl/PnzjD3wenR98ruaBhdo+ACRPn36NPXr19fuJ80dd+nSheXLl/P06VMePnyoPe7l5cW2bdsYPHgws2fPpmDBgvz222/S/U4iMTLxqngeRDzgTvgdnkQ9QYh3j3YT1Yk8iX7yesoiOjjFEXI+63x08ulE48KNMTPRSIyjpWPGR7qG4uo//wm0AuqNgDrfwVs5B8+fP0+jVu2xafcLAKP8vXF1zpsp5inE++5MFiAiIgIHBwfCw8NlZhaJ5AN5EvWETbc3cfPlTe6E3SEwMjDtbmipYGlqSV6rvOS1youLlQsNCjWgqWfT98/9ZhWin8P8ahDzHGoNAb+xyaqcO3eORh+3xLbzAm3ZpXGNsbPM2DWmVdeypHeHRCLRHzdCb7Diygq239ueTJTtzO0o4lgEDzsP7ag3NUwUJuSzzqedsvCw88DJ0um96Z+yJPExcHopHJ2tEWiXUppRdAqcOHECk+qdtftda3hmWKDTgxRpiSQHIYTgRewLHkU+4kzwGbbf287Nlze1x6u5VaNewXoUcSxCUceiuFi5ZE+R/VDio+HU7/DvnNfeG46FoM3vYJayR9k1x2pYF3uiqWptzrhPS2eWtYAUaYkkR/Aq8RUbb23kf1f/x+OoxzrHzEzMqO9Rn+5lulPaOXMFxuio1fDiFgSehEcn4fp2zcgZwLEw1BkK5dvDW9MzFy5cwNWtIC1/O0dwxGt/7ZXdq2Wm9YAUaYkkWxOTEMOf1//kj6t/EBobCmjc1fLZ5KOIQxH8CvvRuHBjHJQORrbUCDw5Dxt7w7PruuV5PKHOMCjXLpk4A5w6dQr/5q2w7/qrTvnJHxriamdpOHtTQYq0RJINeZX4inU31vH75d+14uxu685XZb6iuXdzrMxSXiiWK1CrNdMZ+38CdQKYWUGBiuBRFQp9BEX9UhRngJMnT9K4cWMSClTQluW1seDYyIZYmBkn/L4UaYkkmxAVH8WeB3s4EHiAY0+OEavSLLrwsPOgb/m+NPVq+t6HfzmayCC4uFazKOXZNU1ZqU+h+Wywdnrv6SdOnKBx48ZERERQuM8wAPLZKznxvd97zjQsufiOSiTZh8j4SDps68D9iPvasoK2BelZrifNvZtnXR9kQ5MQCze2w4W/4PZeTcwNAHMbaDoZKnaCNDwYPX78OP7+/kRERFCleSdC/iv3L53fcLanESnSEkkWRwjBqCOjuB9xH2crZ9oWb0v9QvUpkadE7vLMeJOnF+DMcri8AWLDX5cXrAoVOkDpVmDlmKamjh8/TuPGjYmMjKRu3brc92mnPTa2ufEftEqRlkiyOMuvLGd/4H7MTcyZ22AuZZzLGNsk45AYB1c3w8klGk+NJOwLauJrlG8PzkXT3WyePHmwsbHB19eXrVu3UnXKYaLjVbSv6oGpifG/BKVISyRZFCEEW+9uZdbZWQCMqDoidwp0WKBm4cnZ/712nzMx08w3+3YBzzrJlnGnhxIlSrBz/0Fm/PuC0j8GaMu//KhwxuzWE1KkJZIsyO2Xt5l4ciKngk4B8Kn3p3xe/HMjW5WJqNVw9wCc+g1u7nw912xXACp3g0pdwC7fu9t4B4cPHyYmJoaVjxwJiYjjRnBksjpFnN8RbzoTkSItkWQhouKjWHhhIauurUIlVChNlfQo24PuZbrnjvnnsIdwYTWcXwUv778u96oDVXpqAu+bZky2Os7dxaFrTzC1dwWSR+z7o3tVano7Y5IFpjpAirREkiUQQrDnwR6mnJxCyCuNb0EDjwZ8V/U73G3djWydgYmPhmtbNMJ879DrcqW9Zp65SndwKZHhbtRqwbiV+zj6OPE/gX7Niq+qYm9pRgUPxyz3ZShFWiIxMheeXWDG6RmcDTkLQCG7QoysNpJa7rWMbJkBEQIeHtcI85VNEP/GdINXHajQEUo1BwubD2p+79Vglv17D0drC+49i+bq0+TJrP/8yhdrK0tKF7DH3NQ4C1XSghRpicRIPIx4yKyzs9jzYA8ASlMl3cp0o0fZHihNc2j6uLBAzXTGhT8h9O7rcsfCGmEu/wXk+fAHdj9tvcpvR+69t97cdmWpUdz4PtBpQYq0RJLJxCbGMv/8fFZeXUmiSESBghZFW9C/Qn/y22QP4Ug3T87B4RmaaY2kRAHmNlC6pUacC1XPkIfGtotP6f/n2WTln1Vyx1kRzfQZM4i8e456Vcvzz7q/sLTM/BgcH4oUaYkkE7ny/Aojj4zU5v+r5V6Lwb6DKZ6nuJEtMwBCwIOjcHg63Nn/urxwLU1S11KfgjJjHhRCCLouO8XBm890yhd0rEQjn3yYm5qQmJjIpb8tiHAqxd/rV2e7JNdSpCWSTGLHvR18f+R7EtWJOFs5M676OOp61DW2WfpHCLi5SyPOSYtOFKZQpjXUGgz5fPTW1Y9br+kI9MCGxRjQoKjOHLOZmRl//PEHKpUq2wk0SJGWSDKFNdfX8POJnxEIGhZqyLjq44yXdNVQqBLh6ibNtEbIFU2ZqVIzaq7xDTh56aWbp+GvWBRwhxXHHuiUHxxWj8J5NQ8ad+/ezcaNG5k/fz4mJiaYmZlhZpY95S57Wi2RZBOEECy+uJh55zWZqNuVaMf31b7HRJF1vQk+iNt7YdtQePnfQzsLW6j8FVTvD3b6mWcPuBFCv1VniYlPnpdxc/+aWoHetWsXLVq0IC4ujjJlytC/f3+99G8spEhLJAZk5bWVWoHuXa43/Sv0z3J+uBki4RXsGQsn/wuQb+UEH/WFqj3BKo9eungS9oqft11j26WnOuV5bSwY9UkpmpR2w8rCFICdO3fSsmVL4uLiaNGiBT179tSLDcZEirREYiACAgOYdmoaAAMrDaRH2R7GNUjfBF2CDT1fx26u2gsajs3ww8AkAkNj2H7pKYsO3uFlTIK2vF89b75tXCJZ8KPt27fTqlUr4uPjadmyJWvWrMHCwkIvthgTKdISiQG4Hnqd7w59h0DQpngbupfpbmyT9IdaDccXwL7xoIoHG1douQCKNdJrN+P+ucK+6yHafXtLM2a2q0DtYi7JBHrbtm189tlnxMfH89lnn7F69WrMzXNGjG0p0hKJHolJiOGPq3+w9PJSXiW+4iO3j/i+2vc5Z4oj4gls6gt3AzT7xZvCp3PB1kXvXV37b5VgDe+8FHO15ataXtp55zd58eIFX3zxBfHx8bRu3Zq//vorxwg0SJGWSPSCSq1i853NzD83Xxt7o7xLeabXm54zsqaEP4ITv2oC7cdFaPIGNpkIvt3SlPkk3d29SuBJuCY9WJcanu/MkJI3b15WrVrF2rVrWbZsWY4SaJAiLZFkmJsvbzL+3/FcfH4R0CSEHVhpIP6e/tnfi+PJOfh3HlzZCOI/rwq3CvDZEnDR7wIcIQQRsYkA/HHsvrb8oyJ5U6wfHx+vnXP+9NNP+fTTT/VqT1ZBirRE8oHEJsby68VfWX55OYkiERtzG/qW70v7ku2xMM3GD6zUak0M52PzNCsGk/CsDTW+hqKNMrSEOyUO3nxGl6UnUzzmYJV8ZLxp0yaGDRvG7t278fLSj/91VkWKtETyAZx4eoIJxybwMPIhAA0LNWRk1ZHks/nwQPRGJz5ak2n7+EIIvaMpMzGDMm2gej9wK6/3LoUQ3H8Rk6JAKxSwpFPlZOUbN26kbdu2JCYmMmfOHGbOnKl3u7ISUqQlknTy57U/mXRyEgCuVq58X+17GhZuaGSrMkDEE00GlNNL4dVLTZmlg2YxStVeYF/AYF1/NGkfwRFx2v1h/iXoVacIACYKRTIvjr///pt27dqRmJhIhw4dmDZtmsFsyypIkZZI0sG6m+u0At26WGu+rfwtdhZ2RrbqA1CrNUGPziyDGztezzfn8YKP+mkybuvJ3zk1ev7vtI5AO9lY0KO2V6qxndevX88XX3yBSqWiY8eOLF++PNsu9U4POf8KJRI9sen2JiYcmwBAF58ufFv52+znWhcVAuf+gDMrIOyN2BeFamimNEo0AxNTg5ux5cIT9lwN1u5fneCPtUXqcrRu3Trat2+PSqWiU6dOLFu2DFNTw9uZFZAiLZGkgR33djDm6BgAOpTskL0EWq2G+4fg9DK4vhXUGg8KLB006al8u4FryUw1afTmy9rtfwbUfKdAq1QqJk+ejEqlonPnzixdujTXCDRIkZZI3svBwIN8f/h77erBEVVHZA+Bjn6hSU91ZvnrB4EABatohLl0K7CwNopp8Yma7N+jP/GhrLvDO+uampqya9cu5s2bx+jRo3OVQIMUaYnknZx8epIhAUNIFIl8UuQTRn80OmsLtCoB7h6Ei6vh6mbNsm0ACzso1xYqd4P8ZY1qYqJKrY1k19gnX6qv5927dylSRPMQ0dnZmXHjxmWWiVkKKdISSQqo1CpWXlvJ3HNziVfH08CjAT/W/DFrLk5RJcC9g5qErte3vvbQAI3bXOWvNG50Bn4Q+D6EECz/9z6rTjzUllmapzwqXrVqFV26dGHBggX06tUrs0zMkkiRlkje4k7YHUYfHc2l55cAqFewHtPqTsPMJAt9XFSJmnnmKxs1eQPfFGYbF01qqopfgnsl49n4FpcehzN+y1WdMhe75JlSVq5cSZcuXVCr1Zw5cyazzMuyZKF3nURiXBLUCSy7vIxFFxaRoE7AztyOYVWG0bJoy6wxxaFKhPuH3xDm0NfHrJ3B51PNPHPhmpnioZEeXkbH8+k8zerFPNbmdPqoMC0ruier98cff9ClSxeEEPTq1YuFCxdmtqlZDinSEglwI/QGo4+O5lqoJjZy3YJ1Gf3R6KyxgjAsEE4sggt/QcyL1+XWeTUj5iRhNs1aH+c7z6I4evs5CoWC0Ztee3PUKubCkMYlktVfsWIF3bp1QwhB7969WbBgASZ6Xn6eHclad1UiyWSEEKy9sZapp6YSr47HQenAiKoj+NjrY+OPnh+f0QQ3urr59WITKyco1VwjzJ61s5wwAySo1DSZdYg7z6KTHSvqasucLyokK1++fDlfffUVQgj69u3LvHnzpED/R9a7wxJJJhEZH8n4Y+PZdX8XoBk9j6sxDmcrZ+MZpVbBje1wbD48PPa63KuOZiVgUT8wzbqhOOMSVczbf1tHoGsVdcbO0gw3ByuGNy2R4pffvXv3EELQr18/5s2bZ/wvyCyEFGlJruTqi6sMPTiUwMhAzBRmDPIdRGefzsYTh7io/4IbLXidzNXEHMq20YizWznj2JVGhBBcfRrBx3OO6JRfGNs4xSh2bzNu3DiqVKnCxx9ngV8wWQwp0pJcRUxCDIsuLuKPK3+QKBJxs3FjWt1plHfRf4S3NBHx5L9g+ssgNlxTZun4X3CjngYNbqQvgsJj+WjSvmTl8ztUeqdAb9myhYYNG2JtbY1CoeCTTz4xpJnZFinSklxDQGAAE09M5Gm0Jut0o8KNGFt9LA7Kd6940ztCwJOzGnG+vOH1Mu08XlC9vya4kUXyNFFZicjYBL5bf5Edl4OSHWtevgCz2lVIFsHuTRYvXkzv3r2pX78+O3bsQKlM7oon0SBFWpLjCY4OZuKJiewP3A9AAZsCjKw2knoe9TLXkGc34NJ6jTC/uUy7UA2NOJdomuVc51JCCEHZcbuTlX9UxIm/en703umKRYsW0bdvXwAqVKiQIzJ6GxIp0pIczYmnJ/ju0HeExoZipjCjc+nO9C7XG2vzTIpZEfZQI8qXNkDwpdflZpYaL42P+oK7b+bYoifi/ou7kcTa3tWpVMgRs1RCjL7JggUL6N+/PwBDhgzhl19+kXPQ7+GDfFzmz5+Pp6cnlpaWVKtWjZMnU057k8SsWbMoUaIEVlZWeHh4MHjwYGJjYz/IYIkkLQgh+P3S7/Ta04vQ2FBKOpVkbfO1DPYdbHiBjnoGJ5fA7/4wqyzsHacRaBMzKNYYWi2GYbeh9W/ZTqABImITtNtXxvtT1cspTQI9f/58rUAPHTpUCnQaSfdIes2aNQwZMoRFixZRrVo1Zs2ahb+/Pzdu3MDV1TVZ/T///JMRI0awdOlSatSowc2bN+natSsKhYIZM2bo5SIkkjeJjI/khyM/cCDwAAAti7bkh2o/YGlmadiOQ+/Brh80+QGT/JpRgGctKNMafFqAtZNhbTAwATdC6LrslHbfzDRtIvvrr78yYMAAAL777jsmT54sBTqNpFukZ8yYQc+ePenWrRugmV/atm0bS5cuZcSIEcnq//vvv9SsWZMOHToA4OnpSfv27Tlx4kQGTZdIknMj9AZDAobwMPIh5ibmfF/te1oXa21YQVAlwomFsP9nSHylKStQSeM+V7pVtvDQSAsdlhzn3zuvVzyWdXdAaZa2OfQqVarg6OhI7969mTRpkhTodJAukY6Pj+fMmTOMHDlSW2ZiYoKfnx/Hjh1L8ZwaNWqwcuVKTp48SdWqVbl79y7bt2+nU6dOqfYTFxdHXNzrtDoRERHpMVOSS9lyZwsTjk0gVhWLm40bM+vNpLRzacN2GnQJ/vkanpzT7HvWhma/ZHoQfUMSGBpD7akHdMp61SnCyKZpv8ZKlSpx6dIl3N3dpUCnk3SJ9PPnz1GpVOTLpxvPIF++fFy/fj3Fczp06MDz58+pVasWQggSExPp06cP33//far9TJo0ifHjx6fHNEkuZ8nFJcw5NweAmgVqMrn2ZBwtHQ3XYUIsHJoKR2drXOiUDtD4R6jUWZPmOhsTm6Bi1KbL3AiK5NLj8GTHz49phKP1+z0y5s2bh6+vL9WrVwegYMGCerc1N2DwxfEBAQFMnDiRBQsWcPbsWf7++2+2bdvGjz/+mOo5I0eOJDw8XPsXGBhoaDMl2Zh/7vyjFeieZXsyv+F8wwr0/aOwqCYcnq4R6FKfwoCT4Nsl2wt0cEQsfjMOsv7Mo2QCXcHDkXuTmqVJoKdPn87XX3+Nv7+//PxmkHSNpJ2dnTE1NSU4OFinPDg4mPz586d4zujRo+nUqRM9evQAoGzZskRHR9OrVy9++OGHFIOoKJVK6dwuSRPHnhxj7NGxAHQr041vKn1juM5iw2HPWM3qQADb/PDxLxpXumxOTHwi7X49nkyYp7Yuh7erDRU98mDyjsUpb/LLL78wbNgwAAYNGiRH0BkkXSJtYWGBr68v+/bto2XLlgCo1Wr27dunfXL7NjExMcmEOClHmRDiA0yWSDTcCL3B4IDBJIpEmno2ZVClQYbpKCoETv2m+UsKFVqpCzSaAFaOhukzE7kdEonfjEM6ZfaWZmz7pjYeTulzV5w6dSrDhw8HYOzYsbk25ZU+Sbd3x5AhQ+jSpQuVK1ematWqzJo1i+joaK23R+fOnXF3d2fSpEkANG/enBkzZlCxYkWqVavG7du3GT16NM2bN891CSUl+iMoOoh++/oRnRBN5XyV+anWT/pPbRVyTRON7uJaUP33INvJG5rPBq/a+u3LSKw59ZDhG14vslGamfDviAbktU3/L9nJkydrnQrGjRvH2LFj9WZnbibdIt2uXTuePXvGmDFjCAoKokKFCuzcuVP7MPHhw4c6I+dRo0ahUCgYNWoUjx8/xsXFhebNm/Pzzz/r7yokuYrI+Ej67etHSEwI3g7ezKo/CwtTPS0tFgLuHtCI8+29r8vdK0ONAVCyeZaM4ZweYhNURMUlMn33Df46+Xq+uE9db0akw2PjTdauXasV6PHjxzNmzBi92CoBhcgGcw4RERE4ODgQHh6Ovb29sc2RGJlRR0ax+c5mnK2cWdVsFQVs9eCHnBiniatxbD6EXNGUKUyg5CdQfQB4VM32DwUBVh5/wKg3sqRoy7tXo1axD4+jHRsbS6tWrahZsyajRo3KiIm5hrTqWvYeEkhyHWeCz7D5zmYUKJhZb2bGBTomFE7/rlnGHfXfA3FzG6jUCar1ASevjBudRRi96TJ/HH+QrPzoiAa4O1p9UJtCCBQKBZaWlmzZsgUzMykp+ka+opJsQ4I6gZ+O/wRA6+KtqeBa4cMbe35bE2D//J+vVwnaFYBqvcG3a454IPgmG8890hHo9X2qU9kzY0vUJ0yYQEREBNOmTUOhUEiBNhDyVZVkG/689ie3w27jqHRkYMWBH9ZIQizsm6ARaP6b6XMrD9W/htIts3RqqowweM0F7XbA0Hp4OmcsXvX48eO1nhsff/wx9evXz1B7ktSRIi3JFgRFB7Hg/AIAhvgO+bDFKkGX4e+eEHJVs1+8qeZhYOGaOWK+OTXOPHip3R7mXyJDAi2EYNy4cUyYMAGAKVOmSIE2MFKkJVme4Ohg+u/rT0xiDOVdytOiaIv0NaBWw/H5mhG0Kh5sXKDFfCjubxiDswhRcYlcfhzOF4uPa8s6VC30we0JIRg7dqx2tfC0adMYOnRohu2UvBsp0pIsze2Xt+mztw/BMcE4WzkzoeaE9PlDhz+CjX3g/mHNfolm0HwO2LoYxuAsQvO5R5KtHpzSuix5bD7MVVEIwejRo7Wus9OnT2fIkCEZtlPyfqRIS7Isp4JOMXD/QCITIvG092RRo0W427qnvYFL62HbEM1ybnNraDJJs1IwB09tAAxecz6ZQI9t7kO7Kh8+ij5//jwTJ04ENOGKBw8enCEbJWlHirQkS7Lz3k6+P/I9CeoEKrpWZG6DuWlPGBsZDLt/gEvrNPvuvvDZEsjrbTiDjcireBWLD91l4cHbxCbopra6PN4fW2XGP+YVK1Zk+fLlvHz5koEDP/ChreSDkCItyXL8cfUPpp6aCoBfIT8m1Z6Utqwq8dHw7zxN+NCEaFCYQp1hUGdojvXaACg1ZmeK5QFD62VIoIUQREZGahdadO7c+YPbknw4UqQlWQYhBDPPzmTZZU2UuQ4lO/Bdle8wfV8GbbUKzq2EAxMhKkhT5u4LTadCwcoGttp4qNWCu8+jdMoGNixG07L5Ke5ql+aodSkhhGD48OFs2bKFAwcOpBrlUmJ4pEhLsgQJ6gTG/TuOf+78A8CgSoP4qsxX787iIYQmvsaeMa/d6hwLg99YKP1Zjp57VqsFLRcc5eKj13PPdyc2y5AwJyGEYNiwYUyfPh2AvXv38uWXX2a4XcmHIUVaYnRiE2MZEjCEw48PY6owZVyNcbQs2vLdJz29ALtHw72Dmn1LR6j7HVTpAWY5Pxb52tOBOgL9afkCehPooUOHapNEL1iwQAq0kZEiLTE6k09O5vDjw1iaWjK93nTqFKyTeuXwR7D/J7iwGhBgaqFZyl37W7DKk2k2G4MnYa9YefwBCwLu6JTf+KlJmhPCvgshBEOGDGHWrFkALFy4kD59+mS4XUnGkCItMSr7Huxjw60NKFAwu8FsahSokXLF2HA4MhOOL4TEWE1ZmTbQcDTk8cw0e41BTHwif599nGL0umVdq+hNoAcPHszs2bMB+PXXX+nVq1eG25VkHCnSEqMRHB3M2GOvU1+lKNCqBDi9DA5Ofp0VpXBNTdJXd99MtDbziYpL5MitZ/RZeVan3NrClK9qejGgQVEszfWTOOPly5ds3boVgMWLF9OzZ0+9tCvJOFKkJUZBLdT8cOQHwuPC8cnrw4AKb6VfEwKubYG94yD0v5/3eYtpUlaVaJqjHwom0XbRMa4+jdApm9G2PJ9V0n/OQCcnJw4cOMCRI0do37693tuXfDhSpCVGYfmV5ZwIOoGVmRVTak/B/E0/5sBTsHsUBP4Xc8LGBeqN0KwWzMH+zgAJKjVbLjzh6O0XOgLdvHwB5nxR4d3eLulErVZz9uxZKlfWuCl6eHhIgc6CSJGWZCovY1+y+OJi/rr+FwAjq47E08FTczD0LuwdD1c3afbNrDRR6moOBKWdUezNbDaefcx3Gy7qlF0c1xh7S/1+OanVavr378+SJUv466+/+Pzzz/XavkR/SJGWZApxqjj+uPoHv1/6nagEzQKMz4p9pnG1iwqBQ9M0c8/qBEABFTpCgx/AXg+psbIBiSo1s/beYt6B29qyxj756FbTyyAC3bdvXxYvXoxCoSAmJkav7Uv0ixRpicEJig7im/3fcC30GgAlnUoy2HcwNZxKa1YJHpuvWcYN4N1QM++cv4wRLc48hBAsPHiHqTtv6JTP71CJj8u56b0/tVpNnz59WLJkCQqFghUrVtCpUye99yPRH1KkJQblfMh5Bh0YxIvYF+RR5mFYlWF87NEQk9NL4Y+O8CpUU9HdFxqOhSJ1jWtwJnMuMCyZQK/rU50qGUxtlRJqtZrevXvz22+/YWJiwooVK+RClWyAFGmJwdh0exMTjk0gQZ1A8TzFmVt3JgXuHITNVSDikaaSc3FoMBpKNc8VHhtv89mCf7XbU1uXo2VFdyzM0hEvO42o1Wp69erF77//jomJCX/88QcdOnTQez8S/SNFWqJ3EtWJzDwzk/9d/R8ADQs1ZKJzTaz/aA3Pb2oq2btDvZFQvj2Y5s63YYLqdVjRZmXz07aKh8H6UigUWFhYYGJiwqpVq/jiiy8M1pdEvyiEEMLYRryPiIgIHBwcCA8P14ZNlGRNIuIj+O7gdxx9chSAvoWa0ef2SUwe/7cgwyoP1B6qibFhnobwozkUIQReI7dr98+PaYSj9YdlTUkrarWaM2fOUKVKFYP2I0kbadW13DmEkeidBHUCO+/tZOGFhQRGBmJpquQnhSv+BxdpKpjbQPX+Gpc6yzQG78+hJKrULP/3vnbf2VZpEIFWqVQsXLiQXr16aUfRUqCzH1KkJRniVeIrNt7ayIorK3gS/QSA/FYuzAmNoVTQUTAxh8rdNMH3bV2NbK1xEUJQ/5cA7r/QdXk7PcpP732pVCq6du3KypUrOXToEGvWrNHrQhhJ5iFFWvJBhMeFs/r6alZdW8XLuJcAOFk60alwU9odX4ldWCDYuELHdVCggnGNzSLsvx6STKDHNvfRez+JiYl06dKFP//8E1NTU9q2bSsFOhsjRVqSLkJiQvjj6h+svbGWmESN4LjbutOtdDdaKAtgubYTvHoJeYvClxtyfIS6tBIRm0D3Fae1+2dHN8LpAzN3v4vExEQ6d+7MX3/9hZmZGatXr6Z169Z670eSeUiRlqSZG6E36Lyjs1aci+cpTvcy3Wns2Rizm7tgVRtNGFH3ytBhLdjkNbLFWYflR+9rtye2Kmswge7UqROrV6/GzMyMtWvX0qpVK733I8lcpEhL0kRsYizDDw0nJjGGUk6lGFBxALXda2t+Rp9eCtu+BaGG4k2gzTKwsDa2yVmGS4/CmbFH43poZW5Kh2qFDNJPr169tAK9bt06WrZsaZB+JJmL/r3mJTmSGWdmcCf8Ds5WzixqtIg6BeugAE2WlK2DNQJdqTO0WyUF+g3OPnxJ83lHtPvzOlQ0WF9dunTB0dGR9evXS4HOQciRtOS9HHp0SBu17qeaP+Fk6aSJ97xjOJz8VVOp3kioOzxXrhp8m9gEFdN33yAoIo4tF55oy79pWIyGpfIZrN+6dety7949HB0dDdaHJPORIi15Jy9evWD00dEAfFnqS2q61wS1GrYPhdO/Awr4ZAZU/sq4hmYBLj8OZ9+1EGbuvZns2MRWZfU+zZGQkED//v355ptvKFNGE5BKCnTOQ4q0JFWEEIz5dwyhsaEUdSzKIN9BGoHeOgjOrgAU0GI+VOxoZEuNz5Un4Xwy90iy8tGf+FDBwxHfwvpNkhsfH88XX3zBxo0b2bFjB7du3cLSMveu4MzJSJGWpMraG2s59OgQFiYWTKkzBaXCDP75Gs6vBIUJtFwE5dsZ20yjolYL1p0JZPiGS9qyqp5O1CzqzIAGRTE10f/0T3x8PO3atWPTpk0olUoWL14sBToHI0VakiK3X95m2ulpAAzyHURxB2/Y1A8urgaFKXy2GMq2MbKVxufU/VAdge5dpwgjm5UyWH/x8fG0bduWzZs3o1Qq2bRpE02aNDFYfxLjI0VakoxLzy4xYP8A4lRxVHerTseSHWBz/9cC3eZ3KC39bx+9jKHd4uPa/XHNfeha08tg/cXFxfH555+zZcsWlEolmzdvxt/f32D9SbIGUqQlOhx6dIihB4fyKvEVpZxKMan2JEyOzoILf4GJmcYH2udTY5tpVP488ZADN0LYczVYW/ZFFQ+DCjTAjz/+yJYtW7C0tGTz5s00btzYoP1JsgZSpCVa/r71NxOOTUAlVNQsUJPp9aZjc/cw7PtRU6HZtFwv0OGvEvh+4yWdMt/CeZj0WVmD9z18+HBOnDjB8OHD8fPTf1AmSdZEirQEIQSLLi5iwfkFAHzq/SnjaozD/MVd2NADEFC5u3SzA56Gv9JuD/MvQSk3OxqUNJzvs0qlwtTUFAA7Ozt2794tgyXlMqRI53IS1Yn8dPwnNtzaAEDPsj35uuLXKGLD4K/2EB8JhWtCk8nGNTSL0On3k9rt/vWLGrSv2NhYWrVqRZ06dRg5ciSAFOhciBTpXExMQgzDDg3j0KNDmChMGFl1JF+U/ALUKljfHULvgIMHtP0fmBk2a0h24MGLaJ5FxgHg7mhl0L5iY2Np2bIlu3bt4tChQ3Ts2JFChQwT80OStZEinUt58eoF/ff158qLKyhNlUytM5UGhRpoDu4bD3f2gZkVfPEn2Dgb19gswKOXMdSdFqDd/7NnNYP19erVK1q2bMnu3buxtrZm+/btUqBzMVKkcyEPIx7SZ28fAiMDcVQ6MrfBXCq4VtAcvLwBjs7WbLecD27ljGZnViE2QUWtKQe0+/VLuFA4r41B+nr16hUtWrRgz5492NjYsH37durUqWOQviTZAynSuYz74ffpsrMLobGhuNu6s8hvEZ4OnpqDQZdg8wDNds2BUEYGiwf47fBd7baHkxWLOvkapJ+YmBhatGjB3r17sbGxYceOHdSuXdsgfUmyDx8UqnT+/Pl4enpiaWlJtWrVOHny5Dvrh4WF0b9/f9zc3FAqlRQvXpzt27e/8xyJ/nnx6gV99/YlNDaUUk6lWNls5WuBvn8E/uoACTHg3QAajjWqrVmJ2ftuabf3DamH0szUIP3s2LGDvXv3Ymtry86dO6VAS4APGEmvWbOGIUOGsGjRIqpVq8asWbPw9/fnxo0buLomTzQaHx9Po0aNcHV1Zf369bi7u/PgwQMZrSuTiUmIYcC+ATyKekRB24Is9FtIXqu88PI+7B4N1/7RVMzjCa1/BxPDCFF24vCtZ4zYcIkElQBgaOPiWJgZLgR769atmTdvHhUqVKBmzZoG60eSvVAIIUR6TqhWrRpVqlRh3rx5AKjVajw8PPj6668ZMWJEsvqLFi1i2rRpXL9+HXNz8w8yMiIiAgcHB8LDw7G3t/+gNnIzKrWKQQGDCAgMwEHpwMqmK/G0zAuHZ8Cx+aCK0wRM8u0G9X+Qaa+AWXtvMmvvLZ2yy+P9sVXqd4YwOjqa+Ph48uTRb5Q8SdYnrbqWrmFBfHw8Z86c0VntZGJigp+fH8eOHUvxnH/++Yfq1avTv39/8uXLR5kyZZg4cSIqlSrVfuLi4oiIiND5k3wYQggmnZxEQGAAFiYWzK03G8+7R2GuLxyZoRFor7rQ54gmLrQUaMJjEnQEekij4pz8oaHeBToqKopmzZrRqFEjwsLC9Nq2JOeQrnfd8+fPUalU5Munu8IqX758XL9+PcVz7t69y/79++nYsSPbt2/n9u3b9OvXj4SEBMaOTXnec9KkSYwfPz49pklSYdmVZay5sQYFCiaX7knFf76FJ+c0B/N4gf/PUKKZzKjyH0IIGs08qN3fNagOJfLb6b2fJIE+fPgw9vb23L17l0qVKum9H0n2x+DeHWq1GldXVxYvXoypqSm+vr48fvyYadOmpSrSI0eOZMiQIdr9iIgIPDw8DG1qjkKlVjHn3ByWXl4KwLDSX9Fo+zhIfAUWdlB3GFTrA2ZK4xqahVCrBbWnHiDkvwUrJfPbGUSgIyMjadasGUeOHMHBwYHdu3dLgZakSrpE2tnZGVNTU4KDg3XKg4ODyZ8/f4rnuLm5YW5uro0/AFCqVCmCgoKIj4/HwiL5SjalUolSKcXjQ4mKj2LE4REcfKQZEfYp15tOF3drBNrjI2j3B9gmf8ib2/n6r3M8Dnsdm2NN7+p67yMyMpKmTZty9OhRHBwc2LNnD1WqVNF7P5KcQ7rmpC0sLPD19WXfvn3aMrVazb59+6hePeU3dM2aNbl9+zZqtVpbdvPmTdzc3FIUaEnGCIwI5MvtX3Lw0UGUpkqm1J5Cf/LAvYNgZgmtFkqBToHnUXFsu/RUu3/y+4Y4WH3Yg+7UiIiIoEmTJhw9ehRHR0f27t0rBVryXtLtTzRkyBCWLFnCihUruHbtGn379iU6Oppu3boB0LlzZ20wGIC+ffsSGhrKwIEDuXnzJtu2bWPixIn0799ff1chAeB66HXab2/PnfA7uFq5srzJcpq5VoZd32sq1BsJTkWMa2QWJCY+kco/7dXuz+9QCVd7/aejevbsGffu3SNPnjzs3buXypUr670PSc4j3XPS7dq149mzZ4wZM4agoCAqVKjAzp07tQ8THz58iInJa+338PBg165dDB48mHLlyuHu7s7AgQMZPny4/q5CwrOYZwzYN4DwuHDKOpdldv3ZuFi7wLpuEBsG+ctB9QHGNjNL8TjsFfP23yLgxjNtWfmCDjQsZZhfGt7e3hw4cIDo6Gg5By1JM+n2kzYG0k/63cQmxtJtZzcuv7iMl4MXK5utxN7CHo4vhJ0jND7QPfdDgYrGNjVLMWPPTea8sZqwqKste4fU1Wsf4eHhXLp0iVq1aum1XUn2J626JmN3ZHPUQs2oo6O4/OIyjkpH5jeYj72ZrWYV4b9zNJXqDJMC/RZPwl5pBfqjIk40LeNG/RL6HUGHhYXh7+/PhQsX2LJlC40aNdJr+5LcgRTpbEy8Kp5pp6ax6/4uzEzMmFlvJh7W+WBjb7i0VlOpwWio/a1xDc1iRMYmUGPyfu1+5cJOdKnhqdc+Xr58SePGjTl9+jR58+ZNMWSCRJIWpEhnU668uMKoI6O4HXYbgDEfjaFynhLw5+dwN0CT1fvTuVCxo3ENzYL8ffaxdrusuwMD/Yrptf2XL1/SqFEjzpw5g7OzM/v27aNcORnyVfJhSJHOZiSoE1hycQmLLy5GJVQ4WToxpvoYGrrXhRXN4eG/YG6jyaZSTCYrTYkElcYd1M7SjC1f63euODQ0lEaNGnH27FmcnZ3Zv38/ZcsaPkmtJOciRTobcfPlTUYdGcW10GsANC7cmFEfjSKPZR44MEkj0Ep76LwZ3KX3QGo8j4oHwK+UfhPIhoeH4+fnx7lz53BxcWH//v2UKVNGr31Ich9SpLMBaqFm2eVlzD8/nwR1Ag5KB0ZVG0UTryaaCg+Pw6Gpmu2PZ0iBfgeLD91h0cE7AMQnqt9TO33Y2NhQokQJHj9+zP79+yldurRe25fkTqRIZ3ES1AmMPjqabXe3AVCvYD3G1hiLs9V/eQdfhcGGniDUUK4dlPvceMZmcabuvM6CgDvafT8f/T7MMzMz448//uDRo0d4enrqtW1J7sVwEcwlGSY2MZZBBwax7e42zBRmjK0+ljkN5rwWaCFg2xAIfwiOhaHZL8Y1OAuz6sQDHYFe27s6rSoWzHC7z58/Z8KECdqwB2ZmZlKgJXpFjqSzKNEJ0fTb24+zIWdRmiqZUW8GdQq+lZD0wmpN4liFqSabiqVc6PMm4a8S6PPHGY7dfaFTvu2bWpQu4JDh9p89e0bDhg25dOkSkZGRTJs2LcNtSiRvI0U6C5KgTuDbgG85G3IWO3M75jWcR6V8b80z3w2A7UM12/VGgIcM1PMmq08+ZMTfl5KV/9rJVy8CHRISQsOGDbl8+TJubm706NEjw21KJCkhRTqLIYTg5+M/c/TJUazMrFjceDFlnN/wEEiIhf0/wjFN+jIK15SLVd7gzINQfjt8jx2Xg7Rl9pZmTG9bAd/CeXCyyXjkxZCQEBo0aMCVK1dwc3PjwIEDlChRIsPtSiQpIUU6i/H75d/ZcGsDJgoTptaZqivQwVc0DwlDrmj2fbtpMqvIpLEIISgxaifxKl2PjV87+eJfOuVY5x9CcHAwDRo04OrVqxQoUIADBw5QvHhxvbUvkbyNFOksxPa725l9djYAw6sMp55HPc0BtRqOL4B940EVD9bO0GIelGhqPGOzGHefR+sI9Oe+BWlcOj8NSurPg0OlUtGkSROuXr2Ku7s7Bw4coFgx/a5WlEjeRop0FuFBxANGHR0FQGefznQo1UFzIPwxbOoD9w5p9os30Sz3loH7tcQnqmm76HUi5Ns/N8XMVP+OS6ampowfP57Bgweza9cuihYtqvc+JJK3kSKdRVh8cTEJ6gQ+cvuIbyv/N8d8ZRNs+QZiw8HcWjO14dtNJo19gxEbLrL6VKBOmSEEOolPP/2UJk2ayKxCkkxD+klnAR5GPNQuVhlUaRAmChN4fBbWddUIdIFK0PswVP5KCvR/3H8ejff325MJ9NERDfTaz5MnT2jUqBF3797VlkmBlmQmciSdBfj14q+ohIra7rUp7fzfUuJ94wEBpZpDm2Vgqt98e9mdb1afQ6V+na/iyPD6FMxjrdc+Hj9+TP369bl16xZdu3bl4MGDKOSXpCSTkSJtZAIjArWj6L7l+2oK7xzQ+EGbmEPjn6VAp0BSPqFirrbsHlxH7+L5pkAXLlyYFStWSIGWGAU53WFkFl/ShByt5V6Lsi5lNeqzb7zmYJXukKewcQ3Mgpy8F8qlx+EAfNu4uN7F89GjR9SrV49bt27h6elJQEAAXl5eeu1DIkkrciRtRAIjAtlyZwvwxij66mZ4cg4sbKH2UCNalzVpv/i4zjLvSoXy6LX9wMBA6tevz507d/Dy8uLAgQMULiy/KCXGQ4q0EVlyaQkqoaKme03K2RWGQ9Pg3/9WElYfALYuxjUwixCboOLEvVA2nXusI9DTPy+Pq72lXvsaOHCgVqADAgIoVKiQXtuXSNKLFGkjERj5xig6wRJmloU4zU948pWFGgOMaF3WIUGl5qNJ+wiLSdApPz3KD2dbpd77W7x4MUII5syZg4eHh97bl0jSixRpI/HbmTkkikRqxMZT/sQyTaFLKU0cjtKtwFTempCIWKpO3KdTVsUzD8P8S+pVoGNiYrC21niGODs7s3HjRr21LZFkFKkEmY0QPA74iX8e7ACFgr6hoZC/HNQZBiU/ARP5LBeg8k97tGmukrg4rjH2lvr1dLl//z4NGjRg+PDh9O7dW69tSyT6QCpCZpLwCjb0YMnlpSQqFFRXW1Ch9SrofQh8PpUCDRy59RzPEdt0BLpWUWfuT/5Y7wJ979496tWrx71795gxYwaxsbF6bV8i0QdyJJ1ZhD+G1R14/OwSmwsWAKBvsyXwdpzoXMrL6Hhm7LnJH8cf6JQbKg7H3bt3qV+/Pg8fPqRYsWLs378fS0v9PoSUSPSBFOnM4OlFWPU5RAXxWz43EhUKPnL7iIpSoAFNDOjWC4/plLWuVJBfPi9nkAUkd+7coX79+gQGBlK8eHEOHDhAgQIF9N6PRKIPpEgbmrsBsPpLiI/kar7ibLJJBJH42i86lxOXqNIR6OL5bJnYqiyVPZ0M0t+dO3eoV68ejx49okSJEhw4cAA3NzeD9CWR6AMp0obkykZNkH51AjGFa/CdPSRGPcKvkF/ydFi5lAPXQ7Tbv3xenja+GU8O+y42bdrEo0ePKFmyJAcOHCB/fv0lBJBIDIEUaUNxex9s6AHqRPBpyc/53Xhwbxv5rPMxrsY4Y1tndA5cD2HUpss8DnulLTO0QAMMGTIECwsLPv/8cynQkmyBdCcwBE/OwdrOGoEu04atldrwz71tmChMmFJnCg7KjCdCzc4IIei2/JSOQPet522w/u7evUtMTAwACoWCr7/+Wgq0JNsgR9L6JvSu5iFhfBR41eVhw+/5cbsmy0qfcn3wzedrZAONS2yCiv6rzmr3W1cqyOeVC1LFQHPQN27coH79+pQsWZKtW7dqF61IJNkFKdL6JC4SVraB6GeQvyyi7R9MOPQtMYkxVHKtRM9yPY1todEQQpCgEtSacoDnUXHackN5cABcv36d+vXrExQUhLOzs87KQokkuyBFWp8cnAqhd8C+IHRcz46nRzkRdAKlqZKfav2EmUnufLnn7rvF9D03k5XvHaL/ONBJXLt2jQYNGhAUFETZsmXZt28fzs7OBulLIjEkuVM1DMGzG5qM3gCfzCBSacO009MA6FG2Bx52uTdYz/qzj3T2bSxMOf59Q+z0vIIwiatXr9KgQQOCg4MpV66cFGhJtkaKtD4QArYP0zwoLN4Uivuz4OQUnr96TmH7wnQr083YFhqFBJWaNacCefBC89Bu8mdlaV6+ADZKw73trl69Sv369QkJCaF8+fLs27ePvHnzGqw/icTQSJHWB9e3wb2DYKqEJpO4HnqdP6//CcD3Vb9Haar/kJpZHSEEHZec4OT9UG1ZVS8ngwo0QFxcHAkJCVSoUIG9e/dKgZZke6RIZxS1GgIma7ZrDAAnL5YEfItaqGlUuBE13GsY175MJjI2gba/Hufa0wid8p9blaGIi63B+69YsSIBAQEULFgQJyfDeIxIJJmJFOmMcmMbBF8CCzuoPoDwuHACAgMA6Fk2d3lzxCaoKDtud7Ly/d/WNahAX7x4kejoaKpXrw5AuXLlDNaXRJLZSJHOCELAwSma7Wq9wdqJ3TfXEa+Op6hjUUo6lTSufZnIsTsv6Pjbce2+rdKMTf1rUNTVzqD9XrhwgYYNGxIfH8+BAwfw9c3dfuiSnIcU6YxwfSsEJY2i+yOEYMPNDQA0925uMPeyrEZ0XCLtl7wWaFc7JSd/8DN4v+fPn8fPz48XL15QpUoVvL0Nt2pRIjEWUqQ/lLCHsGWQZrtaL7B24uTTE1x5cQWlqZIW3i2Mal5m8uvBO9rtdpU9+MavmMH7PHfuHH5+foSGhlK1alV27dqFo6OjwfuVSDIbKdIfQlwU/NUeYp5rUl/V/haApZeXAtCqaCvyWuUer4K/zz3Wbk9pY/j54LNnz+Ln58fLly+pVq0au3btwsEhd8dDkeRcZICl9KJWw8beEHwZbFyh/V9gYcPVF1f598m/mCpM6VK6i7GtzDTiE9U8eqkJlLS0a2WD93ft2jWtQH/00Ufs3r1bCrQkRyNH0uklYJJmLtrUAr5YBQ6a8Jq/XvgVAH9PfwraGT7kZlYgUaWm+Kgd2v28Nob3B/fy8qJatWqEh4ezc+dO7O3tDd6nRGJMPmgkPX/+fDw9PbG0tKRatWqcPHkyTeetXr0ahUJBy5YtP6Rb43N1MxyaqtluPhs8qgJwKugU+wP3Y6owpVe5XkY0MPO49jSCoj/s0CkrXcDwgmlpacnGjRvZtWuXFGhJriDdIr1mzRqGDBnC2LFjOXv2LOXLl8ff35+QkJB3nnf//n2GDh1K7dq1P9hYoxJ6FzYP0GxXHwAVNOFH1ULNtFOaGB2ti7XG2zF3eBiM33JFZ//mT4ZJGAtw8uRJRo8ejRAC0Ai1nZ1hXfskkqxCuj9VM2bMoGfPnnTr1g0fHx8WLVqEtbU1S5cuTfUclUpFx44dGT9+PEWKFMmQwUYhMQ7WdYO4CChUHfzGaw9tu7uNa6HXsDG3oV+FfkY0MvNYefwBx+9qlns3LOnK/ckfY2FmGIE+ceIEjRo14qeffmLRokUG6UMiycqk65MVHx/PmTNn8PN77QNrYmKCn58fx44dS/W8CRMm4OrqSvfu3dPUT1xcHBERETp/RmXPGHh6HqycoPXvYKqZyk9QJTDn3BxAs7owN3h0bLv4lFGbLmv3+zcoarC+jh8/TqNGjYiIiKB27dp06tTJYH1JJFmVdIn08+fPUalU5MuXT6c8X758BAUFpXjOkSNH+P3331myZEma+5k0aRIODg7aPw8PI4b5vLYFTvw3gmv1Kzi4aw9tvbuVoOggXKxc+NLnSyMZmHn8c+EJ/f98nVVlautyVCqUxyB9HTt2jMaNGxMZGUndunXZvn07traGj/0hkWQ1DOqCFxkZSadOnViyZEm64vmOHDmS8PBw7V9gYKABrXwHYQ9hc3/Ndo1voHhj7SG1UGv9ojv7dM4Vke6+XXteu92zthdtqxjmy/Po0aNaga5Xrx7btm2TAi3JtaTLBc/Z2RlTU1OCg4N1yoODg1NM7Hnnzh3u379P8+bNtWVqtVrTsZkZN27cSHEpr1KpRKnMAqL37zyIDYeCVaDhGJ1DBwIPcD/iPnbmdrQp3sZIBmYOd55FsfTIPRJUmgd3w/xL0L++YaY5QkND+fjjj4mKiqJBgwZs2bJFpryS5GrSNZK2sLDA19eXffv2acvUajX79u3TRiB7k5IlS3Lp0iXOnz+v/fv000+pX78+58+fN+40xvtIjINLazXb9UaA6essIkIIll7SjKK/KPkFthY5d5QXHBFLw+kHWXXiobasT13DebA4OTkxd+5cGjduLAVaIuEDFrMMGTKELl26ULlyZapWrcqsWbOIjo6mWzdN9pHOnTvj7u7OpEmTsLS0pEyZMjrnJ8VXeLs8y3FzF7x6CXZuUKS+zqHjT49z8flFlKZKOpTqYCQDDc+B6yF0W35Ku1/Bw5Hxn5bG1ET/gaOEENqAVJ06deLLL7/MNQGqJJJ3kW6RbteuHc+ePWPMmDEEBQVRoUIFdu7cqX2Y+PDhQ0xMcsBq8wt/af6XawsmpjqHfr2oWV34efHPcbbKmbnzrjwJ1xHoMu72bOpf0yB9HTp0iG+//ZYtW7Zop82kQEskGhQiaYVAFiYiIgIHBwfCw8MzZ5VZ1DOYUVKTs7DfCXB9HRf6dNBpuu3qhrmJOTs+20E+m3zvaCh7IoTAa+R27f7QxsUZ0MAwke0OHjxIs2bNiImJoW/fvixYsMAg/UgkWY206pqM3ZESl9ZpBLpAJR2BVgu1dhTdqmirHCnQcYkqJm67pt1vXamgwR4SHjhwgE8++YSYmBj8/f2ZPn26QfqRSLIzUqRT4rwmiWzS0u8EVQJb725l6eWl3I+4j5nCjK/KfmVEA/XP86g4TtwNZdqu69z/L7s3wPS25Q3S3/79+/nkk0949eoVTZo0YePGjVhaWhqkL4kkOyNF+m2eXtTkLDS1gDKtARh1dBTb72l+/ttZ2PGt77e427q/q5VsRd1pB3jwhjAnsbZ3co8dfbB3716aN29ObGwszZo1Y8OGDVKgJZJUkCL9NkkPDEs0BWsnElQJ7HmwB4BvKn5Dh1IdsDG3MaKB+uXf28+TCXT7qoXoU7cIhfPq/zpVKhVDhgwhNjaWjz/+mA0bNmQNn3iJJIsiRfpN4mNeT3WU10x13Aq7RYI6ATsLO3qU7ZGjvA5iE1R0+O2Edv/OxGYGca97E1NTU7Zt28bkyZOZMWOGFGiJ5D3kAF85PXJxDcSGgWNhKNYIgKsvrgJQOm/pHCXQAIduPtNuz/6igkEF+tmz1315eHgwf/58KdASSRqQIp2EEHBC47lB1V5a3+grLzRxk0vnLW0sywyCWi3o9ccZ7X6LCoabY9+5cydeXl6sXbvWYH1IJDkVKdJJ3A2AZ9fA3AYqvo5od+X5fyLtnDNEOiI2gYbTAyjy/Ws/6M99DZfua8eOHbRo0YLo6GjWrVtHNnDLl0iyFHJOOomkUXSFDmDlCECcKo5bYbcA8MnrYyTD9MP959F8t/4iJ++HJjs27lPDfAFt376dVq1aER8fT6tWrfjzzz9z3JSRRGJopEiDJjXWzZ2a7Wq9tcW3Xt4iUZ2Io9KRAjYFjGScfth68UkygT78XX08nAwTwGjr1q20bt2a+Ph4WrduzV9//YW5ufn7T5RIJDpIkQY4sRgQULQROL9e/rz+5noAyjiXydYjwPvPo/ll900AanjnZXiTkpQr6GCwa9qyZQutW7cmISGBNm3a8Oeff0qBlkg+ECnScZFwbqVmu1ofbfGxJ8fYcGsDAN3LpC3tV1YjPlHN1otPGLL2grasXRUPyns4GrTf/fv3k5CQwOeff86qVaukQEskGUCK9MW1EB8JeYuCdwMAYhJiGH9Mk2z2ixJfUDl/ZWNa+MH8dfIhY/95ndV7mH8Jg3pxJDFjxgzKlStHp06dMDOTbzGJJCPkbu8OIeD0Ms125e7wX4jVOefm8DjqMW42bgzyHWQ8+zLIm37QPzQrZbBASaBJeRUfHw9owox269ZNCrREogdyt0g/OqWJ02FmCeW/ACAoOoi/rmuWho+rPi5bLgE/8+AlNSfvZ9/1EAA6VCtEzzpFDNbfhg0bqFevHu3atdMKtUQi0Q+5e6hzWpMCizKtwdoJ0GQAVws1lfNVpoZ7DSMa92E8j4qj9cJ/dco6VC1ksP7Wr1/PF198gUqlwtbWFlNT0/efJJFI0kzuFemYULj8t2a7sibsqBCCLXe2APCp96fGsixDrD6pm4uwaw1P8jsYJsLcunXraN++PSqVik6dOrFs2TIp0hKJnsm9In1jB6jiIF9ZcPcF4EzwGe6G30VpqqRR4UZGNjD9RMUlal3tAEY0LfmO2hljzZo1dOzYEZVKRZcuXfj999+lQEskBiD3zklHPNb8d68ECgVCCOacmwNoRtHZLQP43WdRlBm7S7s/t31Fg/W1du1arUB369ZNCrREYkBy70g6Kljz31aTAuvQo0OcCzmHpaklfcr3eceJWQu1WvDtugtsPPdYW2Zlbkrz8oZbIZkvXz6USiVffPEFS5YsyRmJhyWSLIoUaVtXVGoVs87OAqBjqY64Wrsaz6504jN2J7EJau3+x+XcmNamnEH7rFu3LqdOnaJkyZJSoCUSA5OLRVrjnoatK9vvbed22G3sLOzoVqabce1KI0/CXvHxnMM6Av3PgJqUK+hokP5Wr15N6dKlKVu2LAA+Ptk74JREkl3IxSL9erpj2fmpgGb5t4PSwYhGpY3YBBWNZhwkOl6lLbs7sRkmBgra/7///Y+uXbuSN29ezp49i4eHh0H6kUgkycmdv1UTYiHiKQBqG2fuhd8DoJlXM2NalWYOXA/RCrSd0oxzoxsZTKBXrFhB165dEULQunVr3N1zTgJeiSQ7kDtH0vcPa9zv7NwItXIkUZ2IicIEZ2tnY1uWJvquOqvdvjTe32D9LFu2jO7duyOEoG/fvsybN0/OQUskmUzuFOkb/2UlKdGU4BjNtIezpTPmJlkzWltUXCIPXkQTEhFHt+WntOW9DLjUe+nSpfTo0QMhBP369WPevHnZOlyrRJJdyX0iLYRmIQtAiWYExQQBkM8mnxGNSp1ElRq/6QcJiohNdmyQX7EUzsg4mzdvpnt3TXjWAQMGMGfOHCnQEomRyH0i/fQ8RD7V5DL0rE3wbc3S8HzWWU+kE1RqDlwP0Qq0q52SuEQ17asWYniTEgYTzgYNGlCjRg18fX2ZPXu2FGiJxIjkPpFOGkUXbQDmltqRdH6b/EY0KjkqtaDYDzu0+woF/DuiAWamhp8TtrOzY8+ePVhZWUmBlkiMTO57CnR1s+Z/iY8BeBql8fLISiPpMw9C8X4jmzfAwIbFDCrQv/76K5MmTdLuW1tbS4GWSLIAuWskHXINnl0HUwso0RSA66HXASiax3AB8dPDgoDbTN15Q6fs+o9NsDQ3XGyMhQsX0q9fPwCqVq1Kw4YNDdaXRCJJH7lLpK9s1Pz3bghWjkTFR3E/4j4APnmNu4LueVQcU3deZ+3pR9qyH5qVMmiwfoD58+czYMAAAL799lsaNGhg0P4kEkn6yD0iLcRrkS7dCoBrodcAKGBTACdLJ2NZxqKDd5i847pO2e7BdSiez86g/c6dO5dvvvkGgGHDhjFlyhQ5xSGRZDFyj0iHXIPnN8FUqZ3quPJck6TVWKNolVpQfNQOVGqhLbNTmrGqZzWDC/Ts2bMZNGgQAMOHD2fSpElSoCWSLEjuEemgi5r/HlXB0h6AKy80Il3aubRRTPrr5EMdgf7fV1WpU9zF4P1euHBBK9AjRoxg4sSJUqAlkixK7hHpSI0XBw4FtUUXn2mE21gj6SO3nmu3b//cNFPc6wDKly/PrFmzCAkJ4aeffpICLZFkYXKRSGv8obHT+EM/iXrCk+gnmCpMKe9SPtPNUasFO69obOpRyytTBDo+Ph4LCwsABg4caPD+JBJJxsk9ftJJI2k7N0CTzxA0o2gbc5tMN6f7itcxODycrA3e3y+//EKNGjV4+fKlwfuSSCT6I/eIdESSSGtG0kki7ZvPN9NNCYuJ58CNZ9r9lhUNG/5z2rRpDBs2jDNnzrBu3TqD9iWRSPRL7hHpqP+mO2yNK9JCCKpP2q/dvziuMQ5Whou+N2XKFL777jsAxo0bR69evQzWl0Qi0T+5R6Rj/vuZb52XG6E3uB9xHzMTMyrlq5SpZnRddopXCZqA/T5u9thbGk6gJ02axIgRIwAYP348Y8eONVhfEonEMOQOkU6Mh/hIzba1E1vvbgWgbsG62FvYZ5oZu64EcfDm62mOrV/XMlhfEydO5Pvvvwfgxx9/ZMyYMQbrSyKRGI7c4d0RG/bfhgKVhS3b72qCFzX3bp6pZvyw8ZJ2+8woP4OlvAoNDWX+/PkA/Pzzz1qxlkgk2Y/cIdIxoZr/lg6cCDlNyKsQHJQO1HGvk2kmnA8M43lUPAAtKxQgr63SYH05OTlx4MABdu7cqV32LZFIsie5Q6Rf/TcfbZWHrXc0Ux1NPJtgbpp56bJ+O3xXu/1zq7KoVCoSEhL02seDBw8oXLgwAIUKFaJXr17ExibP6CKRSAyPubk5pqYZj175QSI9f/58pk2bRlBQEOXLl2fu3LlUrVo1xbpLlizhf//7H5cvXwbA19eXiRMnplrfILzSjKRjrPOw9+FeIPOnOp5FxgHwcbn8RIQ+42FYmF7bDwsLIyIigujoaKysrPTatkQi+TAcHR3Jnz9/hlb1pluk16xZw5AhQ1i0aBHVqlVj1qxZ+Pv7c+PGDVxdXZPVDwgIoH379tSoUQNLS0umTJlC48aNuXLlCu7uhvUP1vLfSHqjUsGrxFcUti9MOedymdP3f5y4p/miaOSpJCwsDFdXV70E1hdCEBISQnR0NHnz5sXV1RVn5+yR9VwiyakIIYiJiSEkJAQANze3D24r3SI9Y8YMevbsSbdu3QBYtGgR27ZtY+nSpVp3rzdZtWqVzv5vv/3Ghg0b2LdvH507d/5As9NJXCTxwFLCAOjs0zlT41XsuarJSG5lpsDTToGrqyt58+bNcLtCCJ48ecKzZxqPkYIFC5I/f9ZKAyaR5FaSftGGhITg6ur6wVMf6XLBi4+P58yZM/j5+b1uwMQEPz8/jh07lqY2YmJiSEhIwMkp9fjNcXFxRERE6PxllM12NoSIBFytXGlZtGWG20srarWg5/9OA+BoaYKFqQJr64wvA08S6KdPNSspPTw8pEBLJFmMpM96Rp4/pUuknz9/jkqlIl8+3XyA+fLlIygoKE1tDB8+nAIFCugI/dtMmjQJBwcH7Z+Hh0d6zEyRdXaa+Mxdy3TFwtQiw+2llTn7b2m3J3zqg0Kh0MsUx+PHj3UE+u17IpFIjI8+frFn6mKWyZMns3r1ajZu3IilpWWq9UaOHEl4eLj2LzAwMEP9RqvjuWGh8eRoVLhRhtpKDxcfhTFr72uRrl08+Zz9hxIfr3HnkwItkeRs0jUn7ezsjKmpKcHBwTrlwcHB7/2p/csvvzB58mT27t1LuXLvfminVCpRKvXnR3zpVTBqhQI3hQX5bQw/JXDxURifzjuqU7a+T3W9ta9QKPDy8iJv3rw4ODjorV2JRJL1SNdI2sLCAl9fX/bt26ctU6vV7Nu3j+rVUxehqVOn8uOPP7Jz504qV6784dZ+IOciND7KFcwzJ4/h2wLdv743lT0z1rcQgtDQUITQZHJRKBSZKtCBgYF89dVXFChQAAsLCwoXLszAgQN58eKFXvv5+uuvKVWqVIrHHj58iKmpKf/888972/H09NROLVlbW1O2bFl+++03vdm5fPlyHB0d9dbeh/bTtWtX7XWam5vj5eXFd999J/3jcxDpnu4YMmQIS5YsYcWKFVy7do2+ffsSHR2t9fbo3LkzI0eO1NafMmUKo0ePZunSpXh6ehIUFERQUBBRUVH6u4r3cCH6MQAV7AobvK9rT18/5Oxaw5OrE/wZ5l8yQ20KIXj06BF3797lwYMHWqHOLO7evUvlypW5desWf/31F7dv32bRokXaL+fQ0FC99dW9e3euX7/Ov//+m+zY8uXLcXV1pVmzZmlqa8KECTx9+pTLly/z5Zdf0rNnT3bs2KE3Wz+EcePG0bVrV7222aRJE54+fcrdu3eZOXMmv/76qwymlYNIt0i3a9eOX375hTFjxlChQgXOnz/Pzp07tfOiDx8+1D7QAli4cCHx8fG0adMGNzc37d8vv/yiv6t4D9cSNcJZLq/hcxlO331Tuz3mEx+sLTK2qFMIQWBgoHaKycbGJtPTXfXv3x8LCwt2795N3bp1KVSoEE2bNmXv3r08fvyYH374QVvX09OTiRMn8tVXX2FnZ0ehQoVYvHixTnuBgYG0bdsWR0dHnJycaNGiBffv3wegQoUKVKpUiaVLl+qcI4Rg+fLldOnSBTMzM+Lj4xkwYABubm5YWlpSuHBhJk2apHOOnZ0d+fPnp0iRIgwfPhwnJyf27NmjPR4WFkaPHj1wcXHB3t6eBg0acOHCBe3xCxcuUL9+fezs7LC3t8fX15fTp08TEBBAt27dCA8P145ix40bp6dXO/0olUry58+Ph4cHLVu2xM/PT+c6JdmbD3pwOGDAAB48eEBcXBwnTpygWrVq2mMBAQEsX75cu3///n2EEMn+MvNNnSjUANg4ZNxL5F1cCAxj7zWNmJZxt39vACUhBDHxian+RcclcPPuAx4+CSY2UeBaoCA2DnneeU5a/9I6Gg8NDWXXrl3069cv2UrG/Pnz07FjR9asWaPT3vTp06lcuTLnzp2jX79+9O3blxs3bgAaVyR/f3/s7Ow4fPgwR48exdbWliZNmmgfhnbv3p21a9cSHR2tbTMgIIB79+7x1VdfATBnzhz++ecf1q5dy40bN1i1ahWenp4pXoNarWbDhg28fPlSmz4M4PPPPyckJIQdO3Zw5swZKlWqRMOGDbW/DDp27EjBggU5deoUZ86cYcSIEZibm1OjRg1mzZqFvb09T58+5enTpwwdOjRNr6ehuXz5Mv/++6/OdUqyNzk/dodaDUINmICt/rwrUmLftdcPVCe2Kvve+q8SVPiM2ZWOHkI+wKqUuTrBP02j/Fu3biGESHWeuFSpUrx8+ZJnz55pV5w2a9aMfv36ARqXy5kzZ3LgwAFKlCjBmjVrUKvV/Pbbb9pfBMuWLcPR0ZGAgAAaN25Mhw4d+Pbbb1m3bp12amDZsmXUqlWL4sWLA5pfbMWKFaNWrVooFAptzJI3GT58OKNGjSIuLo7ExEScnJzo0aMHAEeOHOHkyZOEhIRoH1L/8ssvbNq0ifXr19OrVy8ePnzIsGHDKFlSM11VrFgxbdsODg4oFIos4Zu+detWbG1tSUxMJC4uDhMTE+bNm2dssyR6IufHkw69C/w3yrMtYNCuHr18BUDTMvkpV9DRoH1lNumZB3/TeydJyJKWx164cIHbt29jZ2eHra0ttra2ODk5ERsby507dwBNvIPPPvtMO+URERHBhg0b6N69u7bdrl27cv78eUqUKME333zD7t27k9kxbNgwzp8/z/79+6lWrRozZ86kaNGiWjuioqLImzev1g5bW1vu3buntWPIkCH06NEDPz8/Jk+erC1PD4cPH9Zpf+LEiaxatUqn7O1Vuemlfv36nD9/nhMnTtClSxe6detG69atM9SmJOuQ80fST88j+G/aQQ8Rqd7F3+c0DygdrdP2U9PK3JSrE/xTPBYeFs7de3cp5FGIvM4ZX0KeUt9poWjRoigUCq5du0arVq2SHb927Rp58uTBxcVFW2ZurhtdUKFQoFZrppyioqLw9fVNUZjebKN79+40bNiQ27dvc+DAAUxNTfn888+1xytVqsS9e/fYsWMHe/fupW3btvj5+bF+/XptHWdnZ4oWLUrRokVZt24dZcuWpXLlyvj4+BAVFYWbmxsBAQHJ7Ejyphg3bhwdOnRg27Zt7Nixg7Fjx7J69eoUX4fUqFy5MufPn9fuz5kzh8ePHzNlyhRtWUb93G1sbLRfPkuXLqV8+fL8/vvvOl9qkuxLrhDpV//NDVuZGS463Kt4lXb7oyJpc7dTKBSpTjlYu+Ylr6Od0ecW8+bNS6NGjViwYAGDBw/WmZcOCgpi1apVdO6c9lgolSpVYs2aNbi6umJvn3pWnPr16+Pl5cWyZcs4cOAAX3zxBTY2ulnd7e3tadeuHe3ataNNmzY0adKE0NDQFEMOeHh40K5dO0aOHMnmzZupVKkSQUFBmJmZpTqXDVC8eHGKFy/O4MGDad++PcuWLaNVq1ZYWFigUqlSPS8JKysrrYCCJtZ3RESETpk+MTEx4fvvv2fIkCF06NBBRkTMAeT46Y6EJ+dI/E9ArM0zHjMjJWITVJQas1O7X8Y9/f7LSUu94+LitGXGFugk5s2bR1xcHP7+/hw6dIjAwEB27txJo0aNcHd35+eff05zWx07dsTZ2ZkWLVpw+PBh7t27R0BAAN988w2PHj3S1lMoFHz11VcsXLiQY8eOJRsVzpgxg7/++ovr169z8+ZN1q1bR/78+d/pUzxw4EC2bNnC6dOn8fPzo3r16rRs2ZLdu3dz//59/v33X3744QdOnz7Nq1evGDBgAAEBATx48ICjR49y6tQp7dy8p6cnUVFR7Nu3j+fPnxMTE5O+FzUdqFQqzp8/r/N37dq1VOt//vnnmJqaarPzSLI3OVukhSA66LJ219rMMCLdb9VZ7XZRV1uKONu8o3ZyhBA8ePCAp0+fcuPGDe3UQFahWLFinD59miJFitC2bVu8vb3p1asX9evX59ixY+8MlvU21tbWHDp0iEKFCvHZZ59RqlQpunfvTmxsbLKRddeuXQkPD6d06dI6HkSgca+bOnUqlStXpkqVKty/f5/t27djYpL6W9rHx4fGjRszZswYFAoF27dvp06dOnTr1o3ixYvzxRdf8ODBA/Lly4epqSkvXrygc+fOFC9enLZt29K0aVPGjx8PQI0aNejTpw/t2rXDxcWFqVOnpuMVTR9RUVFUrFhR569589TjoZuZmTFgwACmTp2q4yEjyZ4oRGavjPgAIiIicHBwIDw8/J0/kZMRepcnCyrj7+GO0lTJ6S9P6922uEQVJUZpRtFezjYcGFov1bqxsbHcu3cPLy8vbeySJIF+/vy5po3/lntLJJLsT0qf+STSqms5eyT99ALRCs0lGmoU/dvhe9rtn1uWSde5UqAlEsn7yNki/eQ8MSaGnY8OiXgdI8HXM0+az5MCLZFI0kLO9u54ef/1SNoAIh0YGsOKYw8A+KZBUZRmaXfxe/r0qVagixQpkq55XYlEknvI2SKN0I6kbczS9zAvNeIT1bRZ9C8vouJ5HPZKW26VzhgdLi4uhIWFkT9/finQEokkVXK4SEOYqWYk7ah0zFA7QggWBNxh2q4byY7VLJqXtpULpqmNJMzNzSlVqlSmB0uSSCTZi5wv0iaaKQgHZcZiL196HK4j0AoFbOxXE2dbCwrmef9UikqlYvjw4Xz55ZdvtCEFWiKRvJucL9J6Gkm/mQZrZrvytCjv/t4od0kkJibSpUsXjh49SrNmzYiPj39n+jCJRCJJIueL9H+LGxwtHdN9bmBoDJN3XOdGcCS3QzRJCnzc7GlV8f1TG0kkJibSqVMnVq9ejbe3Ny4uLllmJaFEIsn65HiRDjf58JH0+C1X2HtNNzzoiKZpz7KSmJjIl19+yZo1azA3N2f27NnaFO8SiUSSFnK8SL/8L/JdekV64OpzOgI9tXU5iuWzpYJH2tpJSEigY8eOrFu3DnNzc9avX0/Dhg25d+/e+0+WSCSS/8jZi1n4sJH0jaBINp9/ot3fOag2bat4ULFQnjQ/7Nu4caNWoDds2MCnn36aLruzGjIRrS65PRFtWvtNqvPmX61atQxqW04jx4v0yw94cPjo5euIZsdGNqBk/nTEC/mPzz//nPHjx/P333+/MxhOdkAmotUfWTURbb169XTS3umz32XLlmnTjD19+jRNX7KS1+RokVYJQcQHPDg8/eAlAOULOuDmkPZ4vPHx8dqoYwqFgjFjxvDJJ5+k3eAsikxEKxPRZqRfR0dH8ufPr/2Ti7fSR44W6TASEf9NTzhYpN1P+vpTTXbx4Ii499R8TXx8PO3ateOTTz5Je2xhISA+2jh/MhGtTESbA/rNDeToB4dPhOZD72pmg7mp+Xtqa7j3PJoDN54B0Kl68uSmKREfH0/btm3ZvHkzSqWS8+fPU6NGjfefmBADEw2bdzFVvn8CFu9fKi8T0cpEtBntt3379pi+kbpu5cqVtGzZ0uD25RRy9Eg6MC4MgILKtEen23rh9QPDcgXfP/qOi4ujTZs2WoHevHlz2gQ6myET0easRLQTJ07UseHw4cP06dNHp+zhw4d66XfmzJk6WWUaNWqUoevNbeTckbQQhEQ+AjsL3Ow80nzaggDNB9HHzZ7axVzeWTdJoLdu3YqlpSWbN2+mcePGabfR3FozojUGaYwKKBPR5sxEtH369KFt27ba/Y4dO9K6dWs+++wzbVmBAu/+lZfWfvPnz2+wnI65gZwr0lHBqBOiAQvMrNMWp7nn/07zKkGTXNTP590fnLi4OFq3bs22bduwtLRky5Yt+Pn5pc9GhSJNUw7GRCaizZmJaJ2cnHReJysrK1xdXT/YLpkA13Dk3OmOlw9ebyveH+c5LCaePVeDtfudPnr3fHRS4lIrKyu2bt2afoHORshEtDIRbVqQCXANQ84VadKXunH05iva7VM/+OFip3xn/RIlSrB37162bt1Kw4YNP8jC7IJMRCsT0aYFmQDXMOTcRLQPj7N0QxtmOuWhhXcLfqr1U6pVhRB4jdwOgKW5Cdd/bJpivdjYWG7evKnzYCw9vCsppUQiyXnIRLR64vCt59rtVT2qpVjn1atXtGjRglq1anHs2LHMMk0ikeRycr1IX3saQeelJ7X7voWT/3RPEujdu3ejVqtJSEjITBMlEkkuJud6d6SBBJWa1gtfx4joX987WZ2YmBhatGjB3r17sbGxYceOHdSuXTszzZRIJLmYXC3SOy4HEROvcaP6uKwbw/x1Y0XHxMTQvHlz9u/fj62tLTt27JARvCQSSaaSa0U6UaXmm7/Oafd/bFlG53hMTAyffPIJBw4cwNbWlp07d1KzZs3MNlMikeRycu2c9M3gKO32MP8SONnoBoYxNTXFysoKOzs7du3aJQVaIpEYhVw7ku71x2ntdv/6yVdZKZVKNmzYkCGXO4lEIskouXIkPWvvTR69fAWgs2glKiqKBQsWaIMJWVpaSoGWSCRGJUePpF8pNN9Bb4Yp7bvyDDsuB2n3N/fXTGNERkbSrFkzjhw5wtOnT/nxxx8z11iJRCJJgRw9kn5grvkO8ngjCt7OK68FekPf6hRwtCIyMpKmTZty5MgRHBwcsn0+Qokku1KvXj0GDRr0zjqenp7MmjVLr/127do1y8a4zhUi7Wnvqfk/Yps2IcnGfjXwLayJSNakSROOHj2Ko6Mje/fupUqVKkayOOsiE9HqIhPRGi8R7alTp+jVq1eG2nib2bNnpzvHY2aRo0U65r/pDgelA0PWnNc5VsrNXivQ//77L3ny5GHv3r1UrlzZCJZmbWQiWv0hE9FmPBGti4sL1tZpi4eeVhwcHN75ZZiU2s0Y5GiRTiJRJfj73GPt/v3JH2NuoknzdOzYMa1A+/r6GtHKrItMRCsT0Wak3/Qmok1MTGTAgAE4ODjg7OzM6NGjdTIDvT3doVAo+O2332jVqhXW1tYUK1ZM54tApVLRvXt3vLy8sLKyokSJEsyePVunz7enO+rVq8eAAQMYNGgQzs7O+Pv7A5pcjk2bNsXW1pZ8+fLRqVMnnj9/jiHJ0SIdZqq5vPDo1wHpA4bWAzR+0L1798bZ2Zl9+/ZRqVKlTLdPCEFMQoxR/tIa/FAmopWJaDO73xUrVmBmZsbJkyeZPXs2M2bMeO9U1fjx42nbti0XL16kWbNmdOzYUXsf1Wo1BQsWZN26dVy9epUxY8bw/fffs3bt2vfaYWFhwdGjR1m0aBFhYWE0aNCAihUrcvr0aXbu3ElwcLBOhhtDkGO9O8ISogj7L/nlwr2a7N8WpiZ4Or/O7tGpUyc+/fRTHBzSnklcn7xKfEW1P1OOumdoTnQ4gXUaUmjJRLQyEW1G+01vIloPDw9mzpyJQqGgRIkSXLp0iZkzZ9KzZ89Uz+natSvt27cHNPkb58yZw8mTJ2nSpAnm5ubaOOAAXl5eHDt2jLVr175TYIsVK6YTJ/ynn36iYsWKTJw4UVu2dOlSPDw8uHnzpva9qW9y7Eg68JUm47dzouDcA03WjMJOlnTs2JGnT59q6xlLoLMbMhGtTET7of2mNxHtRx99pJOOrXr16ty6deud6crefM/Z2Nhgb2+vfc8BzJ8/H19fX1xcXLC1tWXx4sXvvb63pz8vXLigDROR9Jf0Bf4h7420kmNH0olqzQ01V2vEpU2FfOyfM5S9xw4SGBjIwYMH05yXz1BYmVlxosMJo/WdFmQiWpmINqP9ZkYi2ne951avXs3QoUOZPn061atXx87OjmnTpnHixLs/e2/n1IyKiqJ58+Y69y4JNze3DF5B6nyQSM+fP59p06YRFBRE+fLlmTt3LlWrVk21/rp16xg9ejT379+nWLFiTJkyJc1P6PXF7t8mcenYQZydnZk/f77RBRo0b6S0TDkYE5mIViai1We/aeFt8Tx+/DjFihXTmTJJD0ePHqVGjRraKTj4sJFvpUqV2LBhA56enpiZZd74Nt3THWvWrGHIkCGMHTuWs2fPUr58efz9/XV+WrzJv//+S/v27enevTvnzp2jZcuWtGzZksuXL2fY+PRwad/fuLi4cODAAcqWLZupfWd3ZCJamYg2Leir34cPHzJkyBBu3LjBX3/9xdy5cxk4cOAHt5eUo3PXrl3cvHmT0aNHc+rUqXS3079/f0JDQ2nfvj2nTp3izp077Nq1i27duqXpC/tDSbdIz5gxg549e9KtWzd8fHxYtGgR1tbWyVymkpg9ezZNmjRh2LBhlCpVih9//JFKlSplyoONJIJWDcfV1ZUDBw5QpkyZ958g0UEmopWJaNOCvvrt3Lkzr169omrVqvTv35+BAwdmaPFK7969+eyzz2jXrh3VqlXjxYsXOqPqtFKgQAGOHj2KSqWicePGlC1blkGDBuHo6PjO911GSVci2vj4eKytrVm/fr3O09kuXboQFhbG5s2bk51TqFAhhgwZorPUc+zYsWzatEnHJ/VN4uLiiIuL0+5HRETg4eGRrkS0R04vp++V6bjFq7k014l9q3/Fx8cnbRdqIGQiWokkd5HpiWifP3+OSqVK9qAjX758BAUFpXhOUFBQuuoDTJo0CQcHB+2fh4dHqnVTtTXytchv+WOR0QVaIpFIPoQs6YI3cuRIwsPDtX+BgYHpbqNiifp87fApnQp2pnL50gawUiKRSAxPuh5ROjs7Y2pqSnBwsE55cHBwqk79+fPnT1d90Cw3TVpg8KEULlCcXi3T/kBLIpFIsiLpGklbWFjg6+vLvn37tGVqtVobaCclqlevrlMfYM+ePanWl0gkEslr0u3sN2TIELp06ULlypWpWrUqs2bNIjo6mm7dugGaJ7Pu7u7aYDcDBw6kbt26TJ8+nY8//pjVq1dz+vTpZEF3JBKJRJKcdIt0u3btePbsGWPGjCEoKIgKFSqwc+dO7cPBhw8f6rij1KhRgz///JNRo0bx/fffU6xYMTZt2pSrXeGSVkJJJJKcjT4+6+lywTMWaXVVyeqo1Wpu3bqFqakpLi4uWFhYZImVjxKJRL8IIYiPj+fZs2eoVCqKFSuWzJc6rbqWY2N3ZEVMTEzw8vLi6dOnPHnyxNjmSCQSA2NtbU2hQoUytNhFinQmY2FhQaFChUhMTDToUlKJRGJcTE1NMTMzy/CvZSnSRiApL9zbkbskEonkbbLkYhaJRCKRaJAiLZFIJFkYKdISiUSShckWc9JJXoIRERFGtkQikUj0Q5Kevc8LOluIdGRkJMAHRcOTSCSSrExkZOQ7c61mi8UsarWaJ0+eYGdnly53lqQ41IGBgdl6EUxq5PTrg5x/jfL6sj8feo1CCCIjIylQoMA7/aizxUjaxMSEggULfvD59vb2OfYNAjn/+iDnX6O8vuzPh1zju0bQScgHhxKJRJKFkSItkUgkWZgcLdJKpZKxY8dmOIFAViWnXx/k/GuU15f9MfQ1ZosHhxKJRJJbydEjaYlEIsnuSJGWSCSSLIwUaYlEIsnCSJGWSCSSLEy2F+n58+fj6emJpaUl1apV4+TJk++sv27dOkqWLImlpSVly5Zl+/btmWTph5Ge61uyZAm1a9cmT5485MmTBz8/v/e+HsYmvfcvidWrV6NQKGjZsqVhDdQD6b3GsLAw+vfvj5ubG0qlkuLFi2fp92l6r2/WrFmUKFECKysrPDw8GDx4MLGxsZlkbfo4dOgQzZs3p0CBAigUCjZt2vTecwICAqhUqRJKpZKiRYuyfPnyjBkhsjGrV68WFhYWYunSpeLKlSuiZ8+ewtHRUQQHB6dY/+jRo8LU1FRMnTpVXL16VYwaNUqYm5uLS5cuZbLlaSO919ehQwcxf/58ce7cOXHt2jXRtWtX4eDgIB49epTJlqeN9F5fEvfu3RPu7u6idu3aokWLFplj7AeS3muMi4sTlStXFs2aNRNHjhwR9+7dEwEBAeL8+fOZbHnaSO/1rVq1SiiVSrFq1Spx7949sWvXLuHm5iYGDx6cyZanje3bt4sffvhB/P333wIQGzdufGf9u3fvCmtrazFkyBBx9epVMXfuXGFqaip27tz5wTZka5GuWrWq6N+/v3ZfpVKJAgUKiEmTJqVYv23btuLjjz/WKatWrZro3fv/7d1RSFN9GAbwh9Qzu1hEjM0FJWwQhhaBYWwLhiIIQl42KMYuin2yeRVUosQka4wQb8QMLdQrh4iBqEQ166JaN7pBkCk2rJsmCIGjCdva+105vukUz1lnO/t6f7Cbv/+jz8P03WEezv6RNadUYvvtlkqlSK1W0/j4uFwR8yKlXyqVIrPZTE+fPiWHw6H4IS2249DQEBkMBkokEoWKmBex/dxuNzU1NWWt3bp1iywWi6w5/4TDDOk7d+5QbW1t1prNZqOWlhbJP7dk3+5IJBJYXFxEc3NzZu3IkSNobm5GMBjMeUwwGMzaDwAtLS377i8mKf12i8fjSCaTOHHihFwxJZPa7/79+9Bqtbhx40YhYuZFSseZmRmYTCa43W7odDrU1dXB6/Uq8vMwpfQzm81YXFzMvCUSiUQwPz+P1tbWgmSWmxwzpiRusJTL5uYmfv/+DZ1Ol7Wu0+nw5cuXnMdEo9Gc+6PRqGw5pZLSb7e7d+/i5MmTe35plEBKv3fv3uHZs2cIh8MFSJg/KR0jkQgWFhZw/fp1zM/PY21tDS6XC8lkEh6PpxCxD01Kv2vXrmFzcxOXL18GESGVSqG9vR1dXV2FiCy7/WbM1tYWtre3cfToUdHfs2TPpNnBfD4f/H4/nj9/jsrKymLHyVssFoPdbsfIyAg0Gk2x48gmnU5Dq9VieHgY9fX1sNls6O7uxpMnT4od7Y94+/YtvF4vHj9+jKWlJUxPT2Nubg69vb3FjqZYJXsmrdFoUFZWho2Njaz1jY0NVFVV5TymqqpK1P5iktJvR19fH3w+H16/fo3z58/LGVMysf2+fv2K9fV1XLlyJbOWTqcBAOXl5VhZWYHRaJQ3tEhSnkO9Xo+KigqUlZVl1s6ePYtoNIpEIgFBEGTNLIaUfvfu3YPdbsfNmzcBAOfOncOvX7/gdDrR3d194H2VS8F+M+bYsWOSzqKBEj6TFgQB9fX1CAQCmbV0Oo1AIACTyZTzGJPJlLUfAF69erXv/mKS0g8AHj16hN7eXrx48QIXL14sRFRJxParqanBp0+fEA6HM4+2tjY0NjYiHA4r8lN7pDyHFosFa2trmRcgAFhdXYVer1fUgAak9YvH43sG8c4LEv0PbiMky4yR/C9HBfD7/aRSqWhsbIw+f/5MTqeTjh8/TtFolIiI7HY7dXZ2Zva/f/+eysvLqa+vj5aXl8nj8Sj+Ejwx/Xw+HwmCQFNTU/Tjx4/MIxaLFavCgcT2260Uru4Q2/H79++kVqupo6ODVlZWaHZ2lrRaLT148KBYFQ4ktp/H4yG1Wk0TExMUiUTo5cuXZDQa6erVq8WqcKBYLEahUIhCoRABoP7+fgqFQvTt2zciIurs7CS73Z7Zv3MJ3u3bt2l5eZkGBwf/7kvwiIgGBgbo9OnTJAgCNTQ00MePHzNfs1qt5HA4svZPTk7SmTNnSBAEqq2tpbm5uQInFkdMv+rqagKw5+HxeAof/JDEPn//VQpDmkh8xw8fPtClS5dIpVKRwWCghw8fUiqVKnDqwxPTL5lMUk9PDxmNRqqsrKRTp06Ry+Winz9/Fj74Ibx58ybn39ROJ4fDQVardc8xFy5cIEEQyGAw0OjoaF4Z+FaljDGmYCX7njRjjP0NeEgzxpiC8ZBmjDEF4yHNGGMKxkOaMcYUjIc0Y4wpGA9pxhhTMB7SjDGmYDykGWNMwXhIM8aYgvGQZowxBeMhzRhjCvYvMUwN//VnkrwAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 400x400 with 1 Axes>"
       ]
@@ -1893,25 +3602,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/xadupre/install/scikit-learn/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
-      "  warnings.warn(\n",
-      "/home/xadupre/install/scikit-learn/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5.\n",
-      "  warnings.warn(\n"
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/home/xadupre/vv/this312/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n",
+      "\n",
+      "Increase the number of iterations to improve the convergence (max_iter=100).\n",
+      "You might also want to scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-7 {\n",
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1923,31 +3683,32 @@
        "  --sklearn-color-fitted-level-1: #d4ebff;\n",
        "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
        "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
        "\n",
+       "#sk-container-id-4.light {\n",
        "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
        "  --sklearn-color-icon: #696969;\n",
+       "}\n",
        "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1959,7 +3720,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1967,7 +3728,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-container {\n",
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1977,7 +3738,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1993,14 +3754,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2008,28 +3769,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2047,24 +3808,33 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: block;\n",
+       "  display: flex;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2072,27 +3842,25 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2100,79 +3868,78 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
-       "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2182,18 +3949,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2209,23 +3976,25 @@
        "  font-size: smaller;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1em;\n",
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
        "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
        "}\n",
        "\n",
        ".sk-estimator-doc-link.fitted,\n",
        "a:link.sk-estimator-doc-link.fitted,\n",
        "a:visited.sk-estimator-doc-link.fitted {\n",
        "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
@@ -2235,7 +4004,8 @@
        ".sk-estimator-doc-link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2245,7 +4015,8 @@
        ".sk-estimator-doc-link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
@@ -2280,12 +4051,12 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
        "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "  border-radius: 1rem;\n",
        "  height: 1rem;\n",
        "  width: 1rem;\n",
@@ -2295,37 +4066,1186 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;ovrlr&#x27;,\n",
-       "                                LogisticRegression(solver=&#x27;liblinear&#x27;)),\n",
-       "                               (&#x27;rf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StackingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StackingClassifier(estimators=[(&#x27;ovrlr&#x27;,\n",
-       "                                LogisticRegression(solver=&#x27;liblinear&#x27;)),\n",
-       "                               (&#x27;rf&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>ovrlr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;ovrlr&#x27;, LogisticRegression()),\n",
+       "                               (&#x27;rf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StackingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html\">?<span>Documentation for StackingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29\">\n",
+       "            estimators\n",
+       "            <span class=\"param-doc-description\">estimators: list of (str, estimator)<br><br>Base estimators which will be stacked together. Each element of the<br>list is defined as a tuple of string (i.e. name) and an estimator<br>instance. An estimator can be set to 'drop' using `set_params`.<br><br>The type of estimator is generally expected to be a classifier.<br>However, one can pass a regressor for some use case (e.g. ordinal<br>regression).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;ovrlr&#x27;, ...), (&#x27;rf&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('final_estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=final_estimator,-estimator%2C%20default%3DNone\">\n",
+       "            final_estimator\n",
+       "            <span class=\"param-doc-description\">final_estimator: estimator, default=None<br><br>A classifier which will be used to combine the base estimators.<br>The default classifier is a<br>:class:`~sklearn.linear_model.LogisticRegression`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=cv,-int%2C%20cross-validation%20generator%2C%20iterable%2C%20or%20%22prefit%22%2C%20default%3DNone\">\n",
+       "            cv\n",
+       "            <span class=\"param-doc-description\">cv: int, cross-validation generator, iterable, or \"prefit\", default=None<br><br>Determines the cross-validation splitting strategy used in<br>`cross_val_predict` to train `final_estimator`. Possible inputs for<br>cv are:<br><br>* None, to use the default 5-fold cross validation,<br>* integer, to specify the number of folds in a (Stratified) KFold,<br>* An object to be used as a cross-validation generator,<br>* An iterable yielding train, test splits,<br>* `\"prefit\"`, to assume the `estimators` are prefit. In this case, the<br>  estimators will not be refitted.<br><br>For integer/None inputs, if the estimator is a classifier and y is<br>either binary or multiclass,<br>:class:`~sklearn.model_selection.StratifiedKFold` is used.<br>In all other cases, :class:`~sklearn.model_selection.KFold` is used.<br>These splitters are instantiated with `shuffle=False` so the splits<br>will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>If \"prefit\" is passed, it is assumed that all `estimators` have<br>been fitted already. The `final_estimator_` is trained on the `estimators`<br>predictions on the full training set and are **not** cross validated<br>predictions. Please note that if the models have been trained on the same<br>data to train the stacking model, there is a very high risk of overfitting.<br><br>.. versionadded:: 1.1<br>    The 'prefit' option was added in 1.1<br><br>.. note::<br>   A larger number of split will provide no benefits if the number<br>   of training samples is large enough. Indeed, the training time<br>   will increase. ``cv`` is not used for model evaluation but for<br>   prediction.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('stack_method',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=stack_method,-%7B%27auto%27%2C%20%27predict_proba%27%2C%20%27decision_function%27%2C%20%27predict%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27auto%27\">\n",
+       "            stack_method\n",
+       "            <span class=\"param-doc-description\">stack_method: {'auto', 'predict_proba', 'decision_function', 'predict'},             default='auto'<br><br>Methods called for each base estimator. It can be:<br><br>* if 'auto', it will try to invoke, for each estimator,<br>  `'predict_proba'`, `'decision_function'` or `'predict'` in that<br>  order.<br>* otherwise, one of `'predict_proba'`, `'decision_function'` or<br>  `'predict'`. If the method is not implemented by the estimator, it<br>  will raise an error.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for `fit` of all `estimators`.<br>`None` means 1 unless in a `joblib.parallel_backend` context. -1 means<br>using all processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('passthrough',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=passthrough,-bool%2C%20default%3DFalse\">\n",
+       "            passthrough\n",
+       "            <span class=\"param-doc-description\">passthrough: bool, default=False<br><br>When False, only the predictions of estimators will be used as<br>training data for `final_estimator`. When True, the<br>`final_estimator` is trained on the predictions as well as the<br>original training data.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Verbosity level.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>ovrlr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"ovrlr__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('oob_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
+       "            oob_score\n",
+       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"final_estimator__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+       "            solver\n",
+       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-4');</script></body>"
       ],
       "text/plain": [
-       "StackingClassifier(estimators=[('ovrlr',\n",
-       "                                LogisticRegression(solver='liblinear')),\n",
+       "StackingClassifier(estimators=[('ovrlr', LogisticRegression()),\n",
        "                               ('rf', RandomForestClassifier())])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2335,7 +5255,7 @@
     "\n",
     "model = StackingClassifier(\n",
     "    [\n",
-    "        (\"ovrlr\", LogisticRegression(solver=\"liblinear\")),\n",
+    "        (\"ovrlr\", LogisticRegression()),\n",
     "        (\"rf\", RandomForestClassifier()),\n",
     "    ]\n",
     ")\n",
@@ -2344,16 +5264,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7196235911146174"
+       "0.7428493449781659"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2376,11 +5296,16 @@
    "source": [
     "La validation croisée a été escamotée par gain de temps mais faire l'impasse est risquée dans le cas général."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
   },
@@ -2394,7 +5319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/py-base/scrapping.ipynb b/_doc/practice/py-base/scrapping.ipynb
index 18cba6f..006ee7c 100644
--- a/_doc/practice/py-base/scrapping.ipynb
+++ b/_doc/practice/py-base/scrapping.ipynb
@@ -380,7 +380,6 @@
     "import shutil\n",
     "import requests\n",
     "\n",
-    "\n",
     "for e, pokemon in enumerate(liste_pokemon):\n",
     "    print(e, pokemon)\n",
     "    url = \"https://img.pokemondb.net/artwork/{}.jpg\".format(pokemon)\n",
diff --git a/_doc/practice/tds-base/module_file_regex_correction.ipynb b/_doc/practice/tds-base/module_file_regex_correction.ipynb
index 3dfb03c..f858e6d 100644
--- a/_doc/practice/tds-base/module_file_regex_correction.ipynb
+++ b/_doc/practice/tds-base/module_file_regex_correction.ipynb
@@ -275,9 +275,7 @@
     "            return [ math.cos(x) for x in seq ]        \n",
     "        if True :\n",
     "            print (\"Ce message n'apparaît que si ce programme est le point d'entrée.\")\n",
-    "        \"\"\".replace(\n",
-    "        \"        \", \"\"\n",
-    "    )\n",
+    "        \"\"\".replace(\"        \", \"\")\n",
     "    with open(\"monmodule3.py\", \"w\", encoding=\"utf8\") as f:\n",
     "        f.write(code)"
    ]
diff --git a/_doc/practice/tds-base/texte_langue_correction.ipynb b/_doc/practice/tds-base/texte_langue_correction.ipynb
index 095216c..87a56d6 100644
--- a/_doc/practice/tds-base/texte_langue_correction.ipynb
+++ b/_doc/practice/tds-base/texte_langue_correction.ipynb
@@ -82,10 +82,8 @@
    "outputs": [],
    "source": [
     "with open(\"texte.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "    f.write(\n",
-    "        \"\"\"Un corbeau sur un arbre perché tenait en son bec un fromage.\n",
-    "Maître Renard, par l'odeur alléché, Lui tint à peu près ce langage :\"\"\"\n",
-    "    )"
+    "    f.write(\"\"\"Un corbeau sur un arbre perché tenait en son bec un fromage.\n",
+    "Maître Renard, par l'odeur alléché, Lui tint à peu près ce langage :\"\"\")"
    ]
   },
   {
diff --git a/_doc/practice/years/2025/seance4_algo.ipynb b/_doc/practice/years/2025/seance4_algo.ipynb
index 2a5226a..c351d64 100644
--- a/_doc/practice/years/2025/seance4_algo.ipynb
+++ b/_doc/practice/years/2025/seance4_algo.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Algorithmes\n",
+    "# Algorithmes, voyageur de commerce, distance d'édition\n",
     "\n",
     "## Voyageur de commerce"
    ]
@@ -17,11 +17,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.90080623, 0.2350022 ],\n",
-       "       [0.56755441, 0.54858335],\n",
-       "       [0.60316886, 0.99559521],\n",
-       "       [0.87287745, 0.22318813],\n",
-       "       [0.21085165, 0.0701609 ]])"
+       "array([[0.41511992, 0.25980581],\n",
+       "       [0.28882207, 0.18110005],\n",
+       "       [0.43436194, 0.89716744],\n",
+       "       [0.51101688, 0.41073324],\n",
+       "       [0.73591566, 0.73214563]])"
       ]
      },
      "execution_count": 1,
@@ -45,7 +45,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x73893a0a5370>]"
+       "[<matplotlib.lines.Line2D at 0x79588b67de80>]"
       ]
      },
      "execution_count": 2,
@@ -54,7 +54,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -77,7 +77,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x738939c39b50>]"
+       "[<matplotlib.lines.Line2D at 0x79588b2b2a50>]"
       ]
      },
      "execution_count": 3,
@@ -86,7 +86,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -136,18 +136,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mieux -1 3 -0.15191965099971733 [0, 3, 14, 19] []\n",
-      "mieux -1 11 -0.5172511323315206 [19, 14, 3, 0, 16, 6, 10, 17, 15, 11, 18, 2] []\n",
-      "mieux 11 13 -0.0513745444570991 [1, 12] [12, 1]\n",
-      "mieux 11 17 -0.0044994455150375035 [12, 1, 7, 9, 4, 5] [5, 4, 9, 7, 1, 12]\n",
-      "mieux 15 17 -0.03444152981270798 [1, 12] [12, 1]\n",
-      "mieux 11 13 -0.12384307859548493 [5, 4] [4, 5]\n"
+      "mieux -1 2 -0.015414159550566658 [0, 11, 1] []\n",
+      "mieux 3 5 -0.034186569174776416 [13, 3] [3, 13]\n",
+      "mieux 6 14 -0.053147377441806753 [4, 16, 2, 9, 12, 14, 17, 7] [7, 17, 14, 12, 9, 2, 16, 4]\n",
+      "mieux 7 9 -0.01748283747037216 [17, 14] [14, 17]\n",
+      "mieux 13 15 -0.19987294649097015 [4, 19] [19, 4]\n",
+      "mieux 2 4 -0.0075636338480231935 [10, 3] [3, 10]\n",
+      "mieux 6 9 -0.2253313144570693 [7, 14, 17] [17, 14, 7]\n",
+      "mieux 8 10 -0.14891254917547306 [7, 12] [12, 7]\n",
+      "mieux 3 6 -0.0478332555189685 [10, 13, 15] [15, 13, 10]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x738937b2a870>]"
+       "[<matplotlib.lines.Line2D at 0x79588b355be0>]"
       ]
      },
      "execution_count": 4,
@@ -156,7 +159,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 300x300 with 1 Axes>"
       ]
@@ -167,8 +170,10 @@
    ],
    "source": [
     "def elimine_croisements(villes, chemin):\n",
+    "    n_iteration = 0\n",
     "    C = chemin\n",
-    "    while True:\n",
+    "    while n_iteration < 10 + villes.shape[0]:\n",
+    "        n_iteration += 1  # pour éviter une boucle infini\n",
     "        mieux = 0\n",
     "        for i in range(-1, villes.shape[0]):\n",
     "            for j in range(i + 2, villes.shape[0] - 1):\n",
@@ -284,9 +289,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "this312",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/_doc/practice/years/2025/seance5_algo2.ipynb b/_doc/practice/years/2025/seance5_algo2.ipynb
index a0b3ea5..734a5da 100644
--- a/_doc/practice/years/2025/seance5_algo2.ipynb
+++ b/_doc/practice/years/2025/seance5_algo2.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Algorithmes\n",
+    "# Porfilage d'algorithmes\n",
     "\n",
     "## Mesurer le temps"
    ]
diff --git a/_doc/py/c_regex/regex.rst b/_doc/py/c_regex/regex.rst
index 3c5d69a..2b4925c 100644
--- a/_doc/py/c_regex/regex.rst
+++ b/_doc/py/c_regex/regex.rst
@@ -275,7 +275,7 @@ et :func:`re.match` retournent toutes des objets :func:`re.match` :
     date 7 : 08/03/1941     date 8 : 8/1/1980     date 9 : 30/6/1976"""
 
     import re
-    expression = re.compile("([0-3]?[0-9]/[0-1]?[0-9]/([0-2][0-9])?[0-9][0-9])[^\d]")
+    expression = re.compile("([0-3]?[0-9]/[0-1]?[0-9]/([0-2][0-9])?[0-9][0-9])[^\\d]")
     print(expression.search(s).group(1,2)) # affiche ('14/9/2000', '20')
     c = expression.search(s).span(1)       # affiche (9, 18)
     print(s[c[0]:c[1]])                    # affiche 14/9/2000
diff --git a/_latex/ensae/td_note_2010_rattrape.py b/_latex/ensae/td_note_2010_rattrape.py
index acb0016..1aad043 100644
--- a/_latex/ensae/td_note_2010_rattrape.py
+++ b/_latex/ensae/td_note_2010_rattrape.py
@@ -33,11 +33,7 @@ def get_tour():
 Metz	6,11729002	49,0734787
 Sedan	4,896070004	49,68407059
 Grenoble	5,684440136	45,13940048
-Annecy	6,082499981	45,8782196""".replace(
-        ",", "."
-    ).split(
-        "\n"
-    )
+Annecy	6,082499981	45,8782196""".replace(",", ".").split("\n")
     # ligne d'avant : on d�coupe l'unique cha�ne de caract�res
 
     # ligne suivant : on d�coupe chaque ligne en colonne
diff --git a/_unittests/ut_datasets/test_data_ts.py b/_unittests/ut_datasets/test_data_ts.py
index e0acdf0..d8705d3 100644
--- a/_unittests/ut_datasets/test_data_ts.py
+++ b/_unittests/ut_datasets/test_data_ts.py
@@ -4,7 +4,6 @@
 
 
 class TestDataTs(ExtTestCase):
-
     def test_generate_sells(self):
         df = generate_sells()
         self.assertEqual(len(df), 731)
diff --git a/_unittests/ut_tools/test_helpers.py b/_unittests/ut_tools/test_helpers.py
new file mode 100644
index 0000000..3be2c09
--- /dev/null
+++ b/_unittests/ut_tools/test_helpers.py
@@ -0,0 +1,13 @@
+import unittest
+from teachpyx.ext_test_case import ExtTestCase
+from teachpyx.tools.helpers import total_size
+
+
+class TestHelpers(ExtTestCase):
+    def test_total_size(self):
+        res = total_size([4, (5,), {"r": 5}, {4.5}])
+        self.assertGreater(res, 10)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_tools/test_pandas.py b/_unittests/ut_tools/test_pandas.py
new file mode 100644
index 0000000..4833207
--- /dev/null
+++ b/_unittests/ut_tools/test_pandas.py
@@ -0,0 +1,19 @@
+import unittest
+from teachpyx.ext_test_case import ExtTestCase
+from teachpyx.tools.pandas import read_csv_cached
+
+
+class TestPandas(ExtTestCase):
+    def test_read_csv_cached(self):
+        df = read_csv_cached(
+            "https://github.com/sdpython/teachpyx/raw/main/_data/paris_54000.zip"
+        )
+        df2 = read_csv_cached(
+            "https://github.com/sdpython/teachpyx/raw/main/_data/paris_54000.zip"
+        )
+        self.assertEqual(df.shape, df2.shape)
+        self.assertEqual(list(df.columns), list(df2.columns))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_tools/test_profiling.py b/_unittests/ut_tools/test_profiling.py
index 91e8bba..72bfdef 100644
--- a/_unittests/ut_tools/test_profiling.py
+++ b/_unittests/ut_tools/test_profiling.py
@@ -1,7 +1,3 @@
-"""
-@brief      test tree node (time=5s)
-"""
-
 import os
 import sys
 import time
diff --git a/_unittests/ut_xrun_doc/test_measure_time.py b/_unittests/ut_xrun_doc/test_measure_time.py
index d50876a..e4f1f50 100644
--- a/_unittests/ut_xrun_doc/test_measure_time.py
+++ b/_unittests/ut_xrun_doc/test_measure_time.py
@@ -9,6 +9,11 @@ def test_measure_time(self):
         self.assertIsInstance(res, dict)
         self.assertIn("average", res)
 
+    def test_measure_time_max_time(self):
+        res = measure_time(lambda: cos(5), max_time=0.2)
+        self.assertIsInstance(res, dict)
+        self.assertIn("average", res)
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)
diff --git a/clean.sh b/clean.sh
index 73e51b6..16f5457 100644
--- a/clean.sh
+++ b/clean.sh
@@ -4,3 +4,15 @@ rm essai.txt
 rm *.bin
 rm *.prof
 rm *.log
+rm lisezmoi*
+rm etatcivil*
+rm exemple.txt
+rm *.pdf
+rm *.dbf
+rm *.xlsx
+rm _unittests/*/temp_* -rf
+rm _doc/c_data/lisezmoi*
+rm _doc/c_data/etatcivil*
+rm _doc/c_data/exemple.txt
+rm _doc/c_data/*.pdf
+rm _doc/c_data/*.dbf
diff --git a/pyproject.toml b/pyproject.toml
index e114b30..702f8a4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,7 +18,7 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
 ]
-dependencies = ["numpy", "matplotlib", "pandas", "scikit-learn>=1.2"]
+dependencies = ["numpy", "matplotlib", "pandas", "scikit-learn>=1.2", "tqdm"]
 description = "Teaching material, algorithm, machine learning"
 keywords = ["python", "teaching", "algorithmic", "machine learning"]
 license = {file = "LICENSE.txt"}
@@ -68,7 +68,6 @@ dev = [
     "sphinx-gallery",
     "sphinx-issues",
     "sphinx-runpython",
-    "tqdm",
     "ujson",
     "wheel",
 ]
diff --git a/requirements-dev.txt b/requirements-dev.txt
index ac15ba5..e98d633 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -42,7 +42,6 @@ sphinx-issues
 git+https://github.com/sdpython/sphinx-runpython.git
 statsmodels
 torch
-tqdm
 transformers
 ujson
 xarray
diff --git a/requirements.txt b/requirements.txt
index 89986db..b8238d7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,3 +2,4 @@ matplotlib
 numpy
 pandas
 scikit-learn>=1.2
+tqdm
diff --git a/teachpyx/datasets/wines.py b/teachpyx/datasets/wines.py
index e7b534d..eb4caf4 100644
--- a/teachpyx/datasets/wines.py
+++ b/teachpyx/datasets/wines.py
@@ -3,7 +3,6 @@
 import pandas
 from .data_helper import get_data_folder
 
-
 __all__ = ["load_wines_dataset"]
 
 
diff --git a/teachpyx/tools/data_helper.py b/teachpyx/tools/data_helper.py
index dcc3a57..fb287df 100644
--- a/teachpyx/tools/data_helper.py
+++ b/teachpyx/tools/data_helper.py
@@ -26,14 +26,14 @@ def decompress_zip(filename, dest: str, verbose: bool = False) -> List[str]:
                 finalfolder = os.path.split(tos)[0]
                 if not os.path.exists(finalfolder):
                     if verbose:
-                        print(f"creating folder {finalfolder!r}")
+                        print(f"[decompress_zip] creating folder {finalfolder!r}")
                     os.makedirs(finalfolder)
                 if not info.filename.endswith("/"):
                     with open(tos, "wb") as u:
                         u.write(data)
                     files.append(tos)
                     if verbose:
-                        print(f"unzipped {info.filename!r} to {tos!r}")
+                        print(f"[decompress_zip] unzipped {info.filename!r} to {tos!r}")
             elif not tos.endswith("/"):
                 files.append(tos)
         elif not info.filename.endswith("/"):
@@ -45,7 +45,7 @@ def download(
     url: str, dest: str = ".", timeout: int = 10, verbose: bool = False
 ) -> str:
     """
-    Download one file.
+    Downloads one file.
 
     :param url: url
     :param dest: destination folder
@@ -57,13 +57,13 @@ def download(
     dest_zip = os.path.join(dest, filename)
     if not os.path.exists(dest_zip):
         if verbose:
-            print(f"downloads into {dest_zip!r} from {url!r}")
+            print(f"[download] downloads into {dest_zip!r} from {url!r}")
         with urlopen(url, timeout=timeout) as u:
             content = u.read()
         with open(dest_zip, "wb") as f:
             f.write(content)
     elif verbose:
-        print(f"already downloaded {dest_zip!r}")
+        print(f"[download] already downloaded {dest_zip!r}")
     return dest_zip
 
 
@@ -83,12 +83,12 @@ def download_and_unzip(
     dest_zip = os.path.join(dest, filename)
     if not os.path.exists(dest_zip):
         if verbose:
-            print(f"downloads into {dest_zip!r} from {url!r}")
+            print(f"[download_and_unzip] downloads into {dest_zip!r} from {url!r}")
         with urlopen(url, timeout=timeout) as u:
             content = u.read()
         with open(dest_zip, "wb") as f:
             f.write(content)
     elif verbose:
-        print(f"already downloaded {dest_zip!r}")
+        print(f"[download_and_unzip] already downloaded {dest_zip!r}")
 
     return decompress_zip(dest_zip, dest, verbose=verbose)
diff --git a/teachpyx/tools/display/pygame_helper.py b/teachpyx/tools/display/pygame_helper.py
index 56142d3..2b0f028 100644
--- a/teachpyx/tools/display/pygame_helper.py
+++ b/teachpyx/tools/display/pygame_helper.py
@@ -1,7 +1,6 @@
 import math
 from typing import List, Optional, Tuple
 
-
 MOUSE = "mouse"
 KEY = "key"
 
@@ -305,9 +304,7 @@ def build_diff_image(
 
 
 def display_line(ligne: List[Tuple[float, float]], screen, pygame):
-    """
-    Affiche une ligne à l'écran.
-    """
+    """Affiche une ligne à l'écran."""
     color = 0, 0, 0
     for p in ligne:
         pygame.draw.line(screen, color, p, p)
diff --git a/teachpyx/tools/display/video_helper.py b/teachpyx/tools/display/video_helper.py
index 403baaf..cf8102e 100644
--- a/teachpyx/tools/display/video_helper.py
+++ b/teachpyx/tools/display/video_helper.py
@@ -41,14 +41,8 @@ def make_video(
     By default, the video will have the size of the first image.
     It will resize every image to this size before adding them to the video.
     """
-    if len(images) == 0:
-        raise ValueError("No image to convert into a video.")
-    from cv2 import (
-        VideoWriter,
-        VideoWriter_fourcc,
-        imread,
-        resize,
-    )  # pylint: disable=E0401
+    assert images, "No image to convert into a video."
+    from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize
 
     fourcc = VideoWriter_fourcc(*format)
     vid = None
diff --git a/teachpyx/tools/graphviz_helper.py b/teachpyx/tools/graphviz_helper.py
index c919b6b..311bb0f 100644
--- a/teachpyx/tools/graphviz_helper.py
+++ b/teachpyx/tools/graphviz_helper.py
@@ -56,10 +56,7 @@ def find_graphviz_dot(exc: bool = True) -> str:
     return "dot"
 
 
-def run_subprocess(
-    args: List[str],
-    cwd: Optional[str] = None,
-):
+def run_subprocess(args: List[str], cwd: Optional[str] = None):
     assert not isinstance(
         args, str
     ), "args should be a sequence of strings, not a string."
@@ -91,14 +88,14 @@ def run_subprocess(
     p.stdout.close()
     if raise_exception:
         raise RuntimeError(
-            "An error was found in the output. The build is stopped.\n{output}"
+            f"An error was found in the output. The build is stopped.\n{output}"
         )
     return output
 
 
 def run_graphviz(filename: str, image: str, engine: str = "dot") -> str:
     """
-    Run :epkg:`Graphviz`.
+    Runs :epkg:`Graphviz`.
 
     :param filename: filename which contains the graph definition
     :param image: output image
diff --git a/teachpyx/tools/pandas.py b/teachpyx/tools/pandas.py
new file mode 100644
index 0000000..165e544
--- /dev/null
+++ b/teachpyx/tools/pandas.py
@@ -0,0 +1,46 @@
+import hashlib
+import os
+import re
+from pathlib import Path
+from urllib.parse import urlparse, unquote
+import pandas
+
+
+def _filename_from_url(url):
+    parsed = urlparse(url)
+    path = parsed.path
+    name = unquote(path.split("/")[-1])
+    name = re.sub(r"[^\w.\-]", "_", name)
+    assert name, f"unable to create a filename from {url!r}"
+    h = hashlib.sha1(url.encode()).hexdigest()[:8]
+    return f"{os.path.splitext(name)[0]}_{h}.csv"
+
+
+def read_csv_cached(
+    filepath_or_buffer: str, ignore_cache: bool = False, **kwargs
+) -> pandas.DataFrame:
+    """
+    After the data is loaded with :func:`pandas.read_csv`,
+    the data is cached. This is interesting when the data is downloaded.
+    The second call reuses the cached data.
+    The cached dataframe is stored in
+    ``.cache/teachpyx/<filename>_<hash>.csv``.
+
+    :param filepath_or_buffer: Any valid string path is acceptable.
+        The string could be a URL. Valid URL schemes include http,
+        ftp, s3, gs, and file. For file URLs, a host is expected.
+        See :func:`pandas.read_csv`.
+    :param ignore_cache: ignore the cache, overwrites it if it exists
+    :param kwargs: other argument for :func:`pandas.read_csv`
+    :return: dataframe
+    """
+    cache_dir = Path.home() / ".cache" / "teachpyx" / "pandas"
+    cache_dir.mkdir(parents=True, exist_ok=True)
+
+    cache_name = cache_dir / _filename_from_url(filepath_or_buffer)
+    if cache_name.exists() and not ignore_cache:
+        return pandas.read_csv(cache_name)
+
+    df = pandas.read_csv(filepath_or_buffer, **kwargs)
+    df.to_csv(cache_name, index=False)
+    return df
diff --git a/teachpyx/video/tsp_kohonen_pygame.py b/teachpyx/video/tsp_kohonen_pygame.py
index 4c197b9..7c74209 100644
--- a/teachpyx/video/tsp_kohonen_pygame.py
+++ b/teachpyx/video/tsp_kohonen_pygame.py
@@ -1,4 +1,5 @@
 import os
+from tqdm import tqdm
 from ..practice.tsp_kohonen import (
     ENSEMBLE,
     iteration,
@@ -11,9 +12,7 @@
 
 
 def display_neurone(neurones: ENSEMBLE, screen, bn: int, pygame):
-    """
-    Dessine les neurones à l'écran.
-    """
+    """Dessine les neurones à l'écran."""
     color = 0, 0, 255
     color2 = 0, 255, 0
     for n in neurones:
@@ -25,9 +24,7 @@ def display_neurone(neurones: ENSEMBLE, screen, bn: int, pygame):
 
 
 def display_ville(villes: ENSEMBLE, screen, bv: int, pygame):
-    """
-    Dessine les villes à l'écran.
-    """
+    """Dessine les villes à l'écran."""
     color = 255, 0, 0
     color2 = 0, 255, 0
     for v in villes:
@@ -98,14 +95,9 @@ def pygame_simulation(
         wait_event(pygame)
     images = [] if folder is not None else None
 
-    iter = 0
-    while iter < max_iter:
-        iter += 1
-
-        if iter % 1000 == 0:
-            print("iter", iter)
+    for n_iter in tqdm(list(range(max_iter)), desc="optimizing"):
 
-        if iter % maj == 0:
+        if n_iter % maj == 0:
             modifie_structure(neurones, compte_n, tour)
             dist *= alpha
             f2 = tuple(w * beta for w in fs)
@@ -119,16 +111,13 @@ def pygame_simulation(
         empty_main_loop(pygame)
         pygame.display.flip()
 
-        if images is not None and iter % 10 == 0:
+        if images is not None and n_iter % 10 == 0:
             images.append(screen.copy())
 
     if first_click:
         wait_event(pygame)
 
     if folder is not None:
-        print("saving images")
-        for it, screen in enumerate(images):
-            if it % 10 == 0:
-                print("saving image:", it, "/", len(images))
+        for it, screen in enumerate(tqdm(images, desc=f"saving images in {folder!r}")):
             image = os.path.join(folder, "image_%04d.png" % it)
             pygame.image.save(screen, image)
diff --git a/teachpyx/video/tsp_kruskal_pygame.py b/teachpyx/video/tsp_kruskal_pygame.py
index 5e330aa..b430ac9 100644
--- a/teachpyx/video/tsp_kruskal_pygame.py
+++ b/teachpyx/video/tsp_kruskal_pygame.py
@@ -2,6 +2,7 @@
 import math
 import random
 from typing import List, Optional, Tuple
+from tqdm import tqdm
 from ..practice.tsp_kruskal import (
     ENSEMBLE,
     DISTANCE,
@@ -144,9 +145,7 @@ def distance(p1, p2):
 
 
 def display_ville(villes, screen, bv, pygame):
-    """
-    Dessine les villes à l'écran.
-    """
+    """Dessine les villes à l'écran."""
     color = 255, 0, 0
     color2 = 0, 255, 0
     for v in villes:
@@ -156,9 +155,7 @@ def display_ville(villes, screen, bv, pygame):
 
 
 def display_chemin(neurones, bn, screen, pygame):
-    """
-    Dessine le chemin à l'écran.
-    """
+    """Dessine le chemin à l'écran."""
     color = 0, 0, 255
     color2 = 0, 255, 0
     for n in neurones:
@@ -170,9 +167,7 @@ def display_chemin(neurones, bn, screen, pygame):
 
 
 def display_arbre(villes, arbre, mult=1, screen=None, pygame=None):
-    """
-    Dessine le graphe de poids minimal dꧩni par arbre.
-    """
+    """Dessine le graphe de poids minimal dꧩni par arbre."""
     if mult == 2:
         color = 0, 255, 0
         li = 4
@@ -443,10 +438,6 @@ def pygame_simulation(
         wait_event(pygame)
 
     if folder is not None:
-        if verbose > 0:
-            print("saving images")
-        for it, screen in enumerate(images):
-            if verbose > 0 and it % 10 == 0:
-                print(f"saving image: {it}/{len(images)}")
+        for it, screen in enumerate(tqdm(images, desc=f"saving images in {folder!r}")):
             image = os.path.join(folder, "image_%04d.png" % it)
             pygame.image.save(screen, image)