Bagging, or Bootstrap Aggregating, is a powerful ensemble learning technique used in machine learning to improve the accuracy and robustness of predictive models. It involves generating multiple versions of a training dataset through bootstrap sampling and then training a model on each version. The final prediction is obtained by averaging or voting across all individual models. In the context of epidemiology, bagging can be applied to enhance the predictive performance of models used to study disease patterns, risk factors, and intervention outcomes.
