Principle:Scikit learn contrib Imbalanced learn Easy Ensemble

Knowledge Sources	Exploratory Undersampling for Class-Imbalance Learning
Domains	Machine_Learning, Ensemble_Learning, Imbalanced_Learning
Last Updated	2026-02-09 03:00 GMT

Overview

An ensemble method that trains multiple AdaBoost classifiers, each on a different balanced subset obtained by random under-sampling.

Description

EasyEnsemble creates an ensemble of AdaBoost classifiers. For each base learner, a new balanced subset is created by random under-sampling the majority class. This leverages information from the full majority class across the ensemble while each individual AdaBoost learner sees a balanced training set.

Usage

Use this principle when combining the benefits of AdaBoost with balanced sampling across multiple views of the majority class.

Theoretical Basis

For n ensemble members:

1. Random under-sample the majority class to match the minority
2. Train an AdaBoost classifier on the balanced subset
3. Aggregate predictions across all n AdaBoost classifiers

Related Pages

Implemented By

• Implementation:Scikit_learn_contrib_Imbalanced_learn_EasyEnsembleClassifier