Bagging vs Boosting

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You have a high-variance model that overfits the training data. Which ensemble strategy is specifically designed to address this problem?

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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Bagging vs Boosting

Multiple Choice

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