What is Bagging?

Bagging, short for Bootstrap Aggregating, 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 also reduces variance and helps to avoid overfitting. Bagging involves creating multiple subsets of the original data (bootstrap samples), training a separate model on each subset, and then averaging the predictions of all the models (or using a majority vote for classification) to produce a final prediction. It's commonly used with decision trees, but can be used with other models as well.