Bagging

Bagging (Bootstrap aggregation)

Definitions

• It is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
• Bootstrap Aggregating, also known as bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.
• It decreases the variance and helps to avoid overfitting. It is usually applied to decision tree methods. Bagging is a special case of the model averaging approach. Example: Random Forest Classifier