BaggingClassifier
BaggingClassifier is a meta-estimator in machine learning that implements a bootstrap aggregation or bagging technique. Bagging is an ensemble learning method designed to improve the stability and accuracy of machine learning algorithms. It works by training multiple instances of the same base estimator on different random subsets of the training data, drawn with replacement. Each subset is created by bootstrapping, meaning that some samples may be selected multiple times, while others may not be selected at all.
The BaggingClassifier then aggregates the predictions of these individual base estimators to produce a final prediction.
The base estimator can be any classification algorithm, such as decision trees, support vector machines, or