Answer / Mohit Kumar Prasad

The two main paradigms of ensemble methods in machine learning are: (1) Bagging, which creates multiple bootstrap samples from the original dataset and trains a separate model on each sample; (2) Boosting, which trains models iteratively by weighting instances based on their past errors and combining predictions.

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