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Can you explain the contrast between bagging and boosting? And what effects do they have on bias and variance?
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General
Compare how bagging reduces variance through parallel ensemble training, while boosting reduces bias through sequential, error-focused training, with concrete examples like Random Forest vs. AdaBoost.
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