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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

Respuesta :

sarahtaurus17

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.

lhmarianateixeira

In this exercise we have to use the knowledge of algorithm to write the correct alternative that best matches, thus we can say that:

Letter D

The computational complicatedness of K-NN increases as the extent or bulk of some dimension of the training basic document file increase and the treasure gets considerably unhurried as the number of examples and free variables increase.

Also, K-NN happen a non-parametric machine intelligence treasure and as such form no assuming possession about the working form of the question at hand.

The invention everything better accompanying information in visible form of the same scale, therefore standard the information in visible form superior to applying the invention happen urged.

See more about algorithm at brainly.com/question/22952967

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