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Answer: Bagging, because it reduces variance by training multiple models in parallel and averaging their predictions.
Bagging would be the most appropriate ensemble method to address high variance and overfitting issues in this scenario. Bagging reduces variance by training multiple models in parallel and averaging their predictions, which helps in handling overfitting. This method is particularly effective when individual models have high variance, as it stabilizes the predictions and improves overall model performance.
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Consider a dataset with high variance and overfitting issues. Which ensemble method would you choose to address these issues, and why? Provide a detailed explanation.
A
Bagging, because it reduces variance by training multiple models in parallel and averaging their predictions.
B
Boosting, because it reduces bias and variance by training models sequentially.
C
Stacking, because it combines predictions from multiple models and can handle overfitting.
D
None of the above, because ensemble methods are not suitable for this task.
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