Answer Posted / Ankit Singh Chauhan
Bagging (Bootstrap Aggregating) and Boosting are ensemble methods used to improve the performance of machine learning models by combining multiple models. However, they differ in their approach.nnBagging creates multiple subsets of the data by randomly sampling with replacement and trains a separate model on each subset. The final prediction is made by averaging (for regression) or voting (for classification) the predictions of individual models. This helps reduce variance but may increase bias.nBoosting, on the other hand, trains multiple weak models sequentially, where each new model focuses on correcting the mistakes made by the previous one. The final prediction is a weighted sum of the predictions from all models. Boosting reduces bias but can increase variance.
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