Wrapper methods are hyper-parameter selection methods that

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Mr. Dubey

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📊 Machine Learning (ML)

Q. Wrapper methods are hyper-parameter selection methods that

(A) should be used whenever possible because they are computationally efficient
(B) should be avoided unless there are no other options because they are always prone to overfitting.
(C) are useful mainly when the learning machines are “black boxes”
(D) should be avoided altogether.

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✅ Correct Answer: (C) are useful mainly when the learning machines are “black boxes”

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