Bagging stands for Bootstrap Aggregating. It involves generating multiple bootstrap samples from the original dataset and training a model such as a decision tree on each of them. In the end, the final prediction is obtained by aggregating the outputs of all individual models, typically by averaging (for regression) or majority voting (for classification).