Bagging vs Boosting: Parallel Resampling vs Sequential Error Correction
Overview Bagging and Boosting are both ensemble techniques that combine many weak learners into one stronger model, but they build that ensemble in fundamentally different ways. Bagging trains learners independently in parallel on bootstrap samples and averages their outputs to cut variance, while Boosting trains learners one after another, each one correcting the last model’s mistakes through sequential reweighting to cut bias. The choice affects training time, overfitting risk, and how robust the model is to noisy data. ...