Bagging makes use of bootstrap samples in order to train an array of base learners. It then combines their predictions using voting. The motivation behind this method is to produce diverse base learners by diversifying the train sets. In this section, we discuss the motivation, strengths, and weaknesses of this method.
Bagging
Creating base learners
Bagging applies bootstrap sampling to the train set, creating a number of N bootstrap samples. It then creates the same number N of base learners, using the same machine learning algorithm. Each base learner is trained on the corresponding train set and all base learners are combined by voting (hard voting for classification, and averaging for regression). The procedure is depicted...