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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.
