Ensemble Methods Part 2: AdaBoost, XGBoost, Gradient Boosting & Stacking Explained

Part 2: building on mistakes instead of averaging independent opinions.

- AdaBoost — upweight misclassified points, each learner corrects the last
- Gradient boosting — fit trees to RESIDUALS, each tree corrects the ensemble
- XGBoost — regularized, histogram-based, the Kaggle champion
- Stacking — combine diverse model types with a meta-learner
- When to use what — RF for importance, XGBoost for prediction, stacking for competitions

#XGBoost #GradientBoosting #AdaBoost #Stacking #MachineLearning #Kaggle #Bioinformatics

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