Chapter 4 – Ensemble Methods
- This has been left as an exercise for you.
- The main difference between
BalancedRandomForestClassifier
andBalancedBaggingClassifier
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, whileBalancedBaggingClassifier
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.