Answer Posted / Vaibhav Pratap Singh

Ensemble methods, such as bagging and boosting, combine multiple base learners to create a final model with improved performance. By averaging or voting the predictions of these base learners, ensembles reduce variance, minimize overfitting, and increase robustness, resulting in better generalization and higher scores.

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