#ensemble-methods

Master ensemble learning techniques combining multiple models for improved prediction accuracy and robustness. Ensemble strategies: 1. Bagging: bootstrap aggregating, parallel model training, variance reduction. 2. Boosting: sequential model training, error correction, bias reduction. 3. Stacking: meta-learner on base model predictions, cross-validation for meta-features. Random Forest implementation: 1. Hyperparameters: n_estimators=100-500, max_depth=10-20, min_samples_split=2-10. 2. Feature randomness: sqrt(n_features) for classification, n_features/3 for regression. 3. Out-of-bag evaluation: unbiased performance estimate, feature importance calculation. Gradient boosting algorithms: 1. XGBoost: extreme gradient boosting, regularization, parallel processing, GPU support. 2. LightGBM: leaf-wise tree growth, faster training, memory efficient, categorical features. 3. CatBoost: categorical feature handling, symmetric trees, reduced overfitting. Advanced ensemble techniques: 1. Voting classifiers: hard voting (majority), soft voting (probability averaging). 2. Blending: holdout set for meta-model training, simple weighted averaging. 3. Multi-level stacking: multiple meta-learner layers, cross-validation for each level. Feature importance: 1. Permutation importance: feature shuffling, performance degradation measurement. 2. SHAP values: unified feature importance, individual prediction explanations. 3. Gain-based importance: tree-based importance, feature split contribution. Hyperparameter optimization: grid search, randomized search, Bayesian optimization (Optuna), early stopping for boosting methods, validation curves for learning rate and regularization analysis.