What is Ensemble Learning?
Ensemble learning is a machine learning technique that combines several base models in order to produce one optimal predictive model.
Key Types
- Bagging (Bootstrap Aggregating): Fits models on random subsets (e.g., Random Forest).
- Boosting: Fits models sequentially, training each tree to correct errors of the previous ones (e.g., XGBoost, LightGBM).
- Stacking: Combines predictions from multiple models using a meta-learner.