Bagging, short for bootstrap aggregating, is an ensemble learning method in which multiple models are trained independently on random samples of the training data drawn with replacement. Their predictions are then combined, often by averaging or voting, to reduce variance and improve stability.
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 b ootstrap agg regating. See Random forests in the Decision Forests course for more information.