Ensemble Methods

Ensemble methods produce a final prediction by combining several models rather than relying on one. Bagging trains learners on varied samples and averages their outputs; boosting builds learners sequentially to correct earlier errors; stacking uses another model to combine different predictors. Random forests and gradient-boosted trees are familiar examples. Ensembles often improve generalization because their members make partly independent mistakes, but adding similar models provides little benefit. They can also increase compute cost, latency, and interpretive complexity. Effective ensembles require diversity, leakage-free validation, a suitable combination rule, and testing that shows the improvement holds on unseen data rather than only on a benchmark split.