Bagging vs Boosting

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Multiple Choice

Boosting, because it sequentially corrects errors and reduces overfitting Bagging, because it reduces variance by averaging predictions from models trained on different bootstrap samples Stacking, because it uses a meta-learner to combine model predictions Both bagging and boosting equally address high variance

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