Bagging (Bootstrap Aggregating) is an ensemble learning method that improves model stability and accuracy by training multiple instances of the same model on different random subsets of the data. The individual models�?? predictions are then aggregated, typically using majority voting for classification or averaging for regression. Bagging reduces variance and prevents overfitting, making it particularly effective for decision trees, as seen in Random Forests.