Ensemble Methods: Bagging & Boosting

One model is good. A hundred are better. In competitive ML, single models rarely win — ensembles do. Bagging reduces variance by training many trees on random subsets. Boosting reduces bias by letting each new tree correct the mistakes of the last. Today you'll train three regressors on the same housing data and watch the ensemble advantage emerge in the numbers.