Bagging, short for Bootstrap Aggregating, is an ensemble learning technique whereby a group of models is trained on a subset of the training set (sampled with replacement), and the final prediction is an aggregate of those predictions, such as the average (in regression tasks) or the class with the most votes (in classification tasks).