**Problem Detail:**

In K-Nearest Neighbor the value of k decides the accuracy of classification. What are the pros and cons of choosing smaller value for k and larger value for k?

###### Asked By : Daga

###### Answered By : D.W.

There is no simple answer. The standard approach to choose $k$ is to try different values of $k$ and see which provides the best accuracy on your particular data set (using cross-validation or hold-out sets, i.e., a training-validation-test set split).

Intuitively, $k$-nearest neighbors tries to approximate a locally smooth function; larger values of $k$ provide more "smoothing", which or might not be desirable.

Question Source : http://cs.stackexchange.com/questions/49792

**3200 people like this**

## 0 comments:

## Post a Comment

Let us know your responses and feedback