Answer / Narendra Pratap Singh

Ensemble methods combine multiple weak learners to form a strong learner. The general idea behind ensemble methods is to reduce the variance and overfitting by averaging or voting predictions from multiple models. Bagging (Bootstrapped Aggregating) creates multiple subsets of training data with replacement and trains a model on each subset, while boosting sequentially trains models that correct the errors of the previous one.

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