Hands-On Ensemble Learning with Python

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 as follows:

Creating base learners through bagging

By using bootstrap samples with the same size as the original train set, each instance has a probability of 0.632 of appearing in any given bootstrap sample. Thus, in many cases, this type ...