Bagging, which stands for "bootstrap aggregation", is a type of ensemble learning. To bag a weak learner such as a decision tree on a dataset,fitensemblegenerates many bootstrap replicas of the dataset and grows decision trees on these replicas.fitensembleobtains each bootstrap replica by randomly selectingNobservations out ofNwith replacement, whereNis the dataset size. To find the predicted response of a trained ensemble,predicttake an average over predictions from individual trees.

DrawingNout ofNobservations with replacement omits on average 37% (1/e) of observations for each decision tree. These are "out-of-bag" observations. For each observation,oobLossestimates the out-of-bag prediction by averaging over predictions from all trees in the ensemble for which this observation is out of bag. It then compares the computed prediction against the true response for this observation. It calculates the out-of-bag error by comparing the out-of-bag predicted responses against the true responses for all observations used for training. This out-of-bag average is an unbiased estimator of the true ensemble error.