What is Bagging in Machine Learning? A Comprehensive Guide

Bagging, or Bootstrap Aggregating, is a machine learning technique designed to improve the stability and accuracy of models. It involves creating multiple subsets of the training data through random sampling with replacement. Each subset trains a separate model, and their predictions are aggregated—typically by averaging for regression or voting for classification. Bagging helps reduce variance and overfitting by leveraging the diversity among models. A common example of bagging is the Random Forest algorithm, which uses multiple decision trees to make more robust predictions.