Take the 2-minute tour ×  I have implemented k-nearest algorithm
in my system. It consists from 26 classes, each of 100 samples.
In my case, K=7 and it was completely trial and error to get the
best classification result.  I know that K should be chosen
wisely to reduce the noise on the classification. But what about
the number of samples? Is there any general rule such as "the
more samples the better result"? Does it depend on something?
Thank you for all your responses.  share|improve this question
1 Answer 1  You could try considering whatever underlying
mechanism is generating your data, or whatever background
knowledge you have on the problem, which might give you an idea
of the relative size of noise and true underlying variation.
E.g. predicting favourite sports team from location I would
expect more change than predicting favourite sport, so would use
smaller k. However I don't know of much general guidance, except
to use cross-validation.  share|improve this answer  Your Answer