regularization-techniques

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ejikeugba / serp

Smooth Effects on Response Penalty for CLM

penalized-regression ordinal-regression categorical-data proportional-odds-regression regularization-techniques

Updated Nov 25, 2024
R

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

machine-learning simple machine-learning-algorithms polynomial-regression gradient-descent-algorithm stochastic-gradient-descent classification-algorithm batch-gradient-descent mini-batch-gradient-descent regression-algorithms simple-linear-regression lasso-regression-model lasso-regularization ridge-regression-model multiple-linear-regression-model regularization-techniques regression-metrics classification-metrics ridge-regularization

Updated Jul 22, 2025
Jupyter Notebook

onchipuis / Tests_A-Connect

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Library for easy deployment of A-Connect methodology.

deep-learning neural-networks regularization-techniques

Updated Aug 26, 2022
Python

saisriramyerubandi / LogisticRegression

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Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used

neural-networks logistic-regression l2-regularization classification-algorithm l1-regularization regularization-methods regularization-techniques

Updated Jan 20, 2022
Jupyter Notebook

harmanveer-2546 / Guide-to-Regularization

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Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.

numpy inline pandas seaborn matplotlib regularization l1 generalization l2 overfitting regularization-methods overfitting-reduced regularization-techniques regularization-to-avoid-overfitting overfitting-prevention

Updated Sep 8, 2024
Jupyter Notebook

AmritaPanjwani / Logistic_Regression_Hyperparameter_Tuning

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This Jupyter Notebook demonstrates hyperparameter tuning for a Logistic Regression model using Python, with a focus on regularization techniques (L1 and L2). It explains how tuning parameters impacts model performance and helps prevent overfitting in classification tasks.

machine-learning machine-learning-algorithms machinelearning regularization hyperparameter-tuning l2-regularization l1-regularization regularization-methods regularization-hyperparameters regularization-techniques