Ensemble Methods — Machine Learning Roadmap
Combining multiple models to get better predictions than any single one
Steps in Ensemble Methods
- Bagging & Random Forests — intermediate · Training many trees on bootstrapped samples and averaging their predictions
- Boosting (AdaBoost, Gradient Boosting) — intermediate · Sequentially training models that focus on correcting previous models' mistakes
- XGBoost, LightGBM & CatBoost — advanced · Production-grade gradient boosting libraries widely used in real-world ML
- Stacking & Blending Models — advanced · Combining predictions from multiple different model types using a meta-model
Part of
- Machine Learning roadmap — the full learning path