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 Fo
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