Breiman's Bagging Algorithm

In machine learning, Breiman's bagging algorithm is an ensemble learning method and one of the earliest algorithms in this category. It employs the concept of bootstrap aggregating by performing random sampling with replacement from the dataset to generate multiple distinct training subsets. These subsets are then used to train multiple base classifiers (or regressors) for classification or regression tasks. The final prediction is obtained by aggregating the outputs from all base models, typically through majority voting for classification or averaging for regression. One key advantage of bagging is its ability to reduce model variance and enhance generalization performance. The algorithm is relatively simple to implement—commonly using decision trees as base estimators—and easily adaptable to various problem domains. Consequently, it remains one of the most popular ensemble techniques, widely applied in both classification and regression scenarios.