Which of the following statement is true about k-NN algorithm?

k-NN performs much better if all of the data have the same scale

k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large

k-NN makes no assumptions about the functional form of the problem being solved

A) 1 and 2

B) 1 and 3

C) Only 1

D) All of the above

The answer is D. All of the above.

The computational complexity of K-NN increases as the size of the training data set increase and the algorithm gets significantly slower as the number of examples and independent variables increase.

Also, K-NN is a non-parametric machine learning algorithm and as such makes no assumption about the functional form of the problem at hand.

The algorithm works better with data of the same scale, hence normalizing the data prior to applying the algorithm is recommended.