Compare bagging, boosting, and stacking to see how combining models improves predictions
Bagging trains multiple models independently on bootstrap samples and combines them by voting. It reduces variance and works well with high-variance models like deep decision trees.
Boosting sequentially trains models, with each focusing on examples the previous models got wrong. It reduces bias and often achieves higher accuracy but can overfit if not careful.
Stacking uses predictions from multiple diverse base models as features for a meta-model. It combines the strengths of different algorithms and often achieves the best performance.
Tips: Try each method on the same dataset to see how they differ. Notice how bagging creates smoother boundaries, boosting focuses on difficult regions, and stacking leverages model diversity.