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Ensemble Methods Theory
Ensemble Methods Theory
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Bagging and boosting are both ensemble methods that combine multiple base learners. Which statement correctly distinguishes their variance-bias tradeoff behavior?
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A.
Bagging reduces bias while boosting reduces variance
B.
Bagging reduces variance by averaging diverse models; boosting reduces bias by sequentially correcting errors
C.
Neither method has a clear advantage; they perform identically in all settings
D.
Both methods reduce variance by the same amount through model averaging; the only difference between them is computational cost and parallelizability
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