Bagging (bootstrap aggregating): train base learners independently on bootstrap samples and combine by voting or averaging. It mainly reduces variance; Random Forest is a bagging-based method with random feature selection.

Boosting: train learners sequentially so later learners focus on errors or residuals from earlier ones. AdaBoost reweights difficult observations; Gradient Boosting fits learners to loss gradients/residuals.

Bagging	Boosting
Learners can train in parallel	Sequential dependency between rounds
Bootstrap resampling	Reweighting or residual fitting
Primarily reduces variance	Often reduces bias and builds strong predictors
Less sensitive to individual noisy examples in many settings	Can be sensitive to noise/outliers if overfit

Both require validation and sensible tuning to avoid overfitting.