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Answer: Bagging reduces variance, boosting reduces bias, and stacking balances both. Ensemble methods improve performance by addressing the bias-variance tradeoff.
Ensemble methods address the bias-variance tradeoff by improving model performance. Bagging reduces variance by training multiple models in parallel and averaging their predictions, which helps in handling overfitting. Boosting reduces bias by training models sequentially, focusing on correcting errors made by previous models. Stacking balances both bias and variance by combining predictions from multiple models, often using a meta-model, which can improve overall performance by leveraging diverse models.
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Explain the concept of bias-variance tradeoff in the context of ensemble learning. How do bagging, boosting, and stacking address this tradeoff?
A
Bagging reduces variance, boosting reduces bias, and stacking balances both. Ensemble methods improve performance by addressing the bias-variance tradeoff.
B
Bagging increases variance, boosting reduces bias, and stacking increases model complexity. Ensemble methods do not address the bias-variance tradeoff.
C
Bagging and boosting are identical in addressing the bias-variance tradeoff. Stacking is used for reducing model complexity.
D
Ensemble methods do not affect the bias-variance tradeoff. They only increase model complexity.