Chapter 4 – Ensemble Methods

1. This has been left as an exercise for you.
2. The main difference between BalancedRandomForestClassifier and BalancedBaggingClassifier is the base classifier and the ensemble learning method they employ. BalancedRandomForestClassifier uses decision trees as base classifiers and follows a random forest as the estimator, while BalancedBaggingClassifier can use any base classifier that supports sample weighting and follows a bagging approach.

   Random forest can be considered an extension of bagging that incorporates an additional layer of randomness by also randomly selecting a subset of features at each split in the decision tree. This helps create more diverse trees and generally results in better performance of random forest models.