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Consider a scenario where you have a dataset with a large number of features and a relatively small number of samples. Which ensemble technique would you recommend, and why?
A
Bagging, as it reduces the variance of the model and is less prone to overfitting.
B
Boosting, as it focuses on the errors made by previous models and can handle noisy data.
C
Stacking, as it combines the strengths of different models and can handle a large number of features.
D
None of the above, as the dataset is too small for ensemble techniques to be effective.
