I am comparing three methods decision tree, Naïve Bayes, and K-NN. I have a dataset with missing values and by using the Weka value replace tool I was able to replace the values.
I ran two test one before replacing the missing value and one after replacing the missing value. Before replacing the value k-nn correctly classified 55% instances but after replacing missing value it classified 56% correct instances.
I want wondering why this happens to k-nn but not the other methods of classification?