bagging

A method to train an ensemble where each constituent model trains on a random subset of training examples sampled with replacement. For example, a random forest is a collection of decision trees trained with bagging. The term bagging is short for bootstrap aggregating. See Random forests in the Decision Forests course for more information.

Practitioners refer to bagging when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.