Bagging works by creating multiple subsets of the original dataset using bootstrap sampling. Each subset is created by randomly selecting samples from the original dataset with replacement. This means some samples may appear multiple times in a subset, while others may not appear at all. These subsets are then used to train multiple models independently. The final prediction is derived by aggregating the predictions of all individual models, typically through averaging for regression tasks or majority voting for classification tasks.
