Ensemble ML Techniques & Applications

Ensemble Learning: Foundations and the Evaluation Setup

Bagging and Random Forest: Focused Review

Leakage-Safe Validation and Ensemble Tuning

AdaBoost: Learning from Earlier Mistakes

Gradient Boosting: Improving an Additive Model

XGBoost: Regularized Gradient-Boosted Trees

LightGBM: Histogram-Based and Leaf-Wise Boosting

CatBoost: Boosting with Categorical Features

Voting, Averaging and Model Diversity

Stacking with Out-of-Fold Predictions

Choosing and Documenting the Final Ensemble

Capstone: A Reproducible Ensemble Classification Benchmark