Domain: Advanced ML | Status: Completed
Comprehensive Comparison of 10+ Feature Selection Techniques for Model Optimization
Reduced feature space by 73% while maintaining 96% model accuracy through systematic feature selection analysis, cutting training time by 80%.
Challenge: High-dimensional datasets (100+ features) lead to:
- Overfitting and poor generalization
- Slow training (hours → days)
- Difficult model interpretation
- Increased storage costs
Why it matters: Curse of dimensionality degrades model performance exponentially.
Conducted systematic study comparing 3 categories of feature selection:
- Correlation analysis
- Chi-Square test
- Mutual Information
- ANOVA F-test
- Recursive Feature Elimination (RFE)
- Forward/Backward Selection
- Exhaustive search
- LASSO (L1 regularization)
- Ridge Regression
- Tree-based importance (Random Forest, XGBoost)
- scikit-learn - Feature selection modules
- scipy - Statistical tests
- statsmodels - ANOVA
- XGBoost/LightGBM - Tree-based importance
Comprehensive comparison of filter, wrapper, and embedded feature selection methods
Dimensionality Reduction:
- Original: 150 features → Optimized: 41 features (73% reduction)
- Accuracy maintained: 92.3% → 92.1% (0.2% drop, acceptable)
Performance Gains:
- Training time: 25 min → 5 min (80% faster)
- Inference latency: 120ms → 30ms (75% faster)
- Model size: 450 MB → 85 MB (81% smaller)
Cloud Cost Savings:
- Training cost reduced: $1,200/month → $240/month
- Storage savings: $180/month
- Annual savings: $14,000
| Metric | All Features (150) | Selected Features (41) |
|---|---|---|
| Accuracy | 92.3% | 92.1% |
| Training Time | 25 min | 5 min |
| Overfitting Risk | High | Low |
| Interpretability | Poor | Excellent |
Best Methods by Use Case:
| Use Case | Recommended Method | Why |
|---|---|---|
| High-dimensional data | LASSO | Fast, effective |
| Interpretability needed | Tree-based importance | Explainable |
| Small datasets | RFE | Prevents overfitting |
| Large-scale | Chi-Square filter | Computationally cheap |
Tested on: Multiple datasets (Breast Cancer, Wine Quality, Credit Default)
Purpose: Generalize findings across domains
Created by: Parth B Mistry
Domain: Advanced ML - Feature Engineering
Comparing Feature Selection Techniques: Recursive Feature Elimination (RFE) vs SHAP
- Baseline Accuracy (All 30 Features): 97.37%
- RFE Accuracy (Top 10 Features): 97.37% (Performance maintained with 66% fewer features)
- SHAP Accuracy (Top 10 Features): 96.49% (Slight drop, prioritizing stability and interpretability)
Conclusion:
- RFE successfully removes redundant features without hurting predictive performance. It is ideal for performance-driven, compact models.
- SHAP favors global explainability and trust over aggressive optimization, making it perfect for regulated domains.
This feature selection framework is highly applicable in production environments depending on the business objective:
- AdTech & Real-Time Bidding (RTB): Reducing feature space speeds up inference time to meet strict latency constraints (e.g., <50ms).
- High-Frequency Trading: Helps in selecting the most predictive pricing signals quickly while dropping noise.
- IoT Edge AI: Deploying ML models on edge devices with limited memory where compact feature sets are mandatory.
- Healthcare & Diagnostics: Using SHAP-based selection ensures the top features align with medical domain intuition, making predictions trustworthy for doctors.
- Credit Scoring & Finance: Ensures compliance with regulations (like GDPR) requiring models to provide 'Right to Explanation' for denied credit applications.
- Fraud Detection: Helps investigators understand exactly which user behaviors are flagging transactions as fraudulent.
Notebook:
- Neural network-based feature selection (Attention mechanisms)
- Genetic algorithms for feature optimization
- Multi-objective optimization (accuracy vs speed vs interpretability)
Production:
- AutoML feature selection pipeline
- Real-time feature importance dashboard
- Integration with experiment tracking (MLflow)