🎯 Ensemble Methods Lab

Ensemble Learning

Ensemble methods combine multiple models to achieve better predictions than any single model. "Wisdom of crowds" for machine learning!

Types of Ensembles

• Bagging: Train on bootstrap samples, average predictions (Random Forest)
• Boosting: Sequential training, focus on mistakes (XGBoost, AdaBoost)
• Stacking: Train meta-model on base model predictions
• Voting: Simple majority vote or average

Why Ensembles Work

• Different models make different errors
• Averaging reduces variance
• Combines diverse perspectives
• More robust to outliers and noise

Famous Ensemble Methods

• Random Forest: Bagging decision trees
• XGBoost: Gradient boosting trees
• LightGBM: Fast gradient boosting
• CatBoost: Boosting with categorical features
• Stacked Generalization: Meta-learning ensembles

When to Use

• Kaggle competitions (ensembles dominate!)
• When squeeze every % of accuracy matters
• Have computational budget for multiple models
• Production systems with latency tolerance

Trade-offs

• ✅ Higher accuracy
• ✅ More robust
• ❌ Slower inference (N models)
• ❌ More complex deployment
• ❌ Harder to interpret