Figure from the original question paper

(B) k-nearest neighbors is based on computing some distances. Features need to be normalized to contribute approximately equally to the distance computation.

(A) Faster to compute the list of neighbors on scaled data.

(C) Scaling the data has a negligible effect on k-nearest neighbor’s performance, but it enhances the interpretability of the model.

(D) This is irrelevant. One could use k-nearest neighbors without normalizing the dataset and get a very similar cross-validation score.

Machine Learning Practice · End Term · 24 Dec 2023 · September 2023 term · Set FDD1

MLP End Term 24 Dec 2023 — Question 7

Question 7

+2 marksOne correct option

A
Faster to compute the list of neighbors on scaled data.
B
k-nearest neighbors is based on computing some distances. Features need to be normalized to contribute approximately equally to the distance computation.
C
Scaling the data has a negligible effect on k-nearest neighbor’s performance, but it enhances the interpretability of the model.
D
This is irrelevant. One could use k-nearest neighbors without normalizing the dataset and get a very similar cross-validation score.

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Correct answer

B
k-nearest neighbors is based on computing some distances. Features need to be normalized to contribute approximately equally to the distance computation.

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Question 7 of 38 in the IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 24 Dec 2023, in the September 2023 term (IIT M DIPLOMA FN EXAM FDD1 24 Dec 2023). It carries 2 marks.

MLP End Term 24 Dec 2023 — Question 7

Question 7

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