kNNclassify and NaiveBayes both classify sample data on the basis of its closest match. Which can also be thought of finding the euclidean dist between the sample and each of the training data to find the closest match.
What is the difference between each of the classifying function? Is it only on the basis of efficiency or is it different in it working as well.
I have talked about kNN and Naive Bayes because I am familiar only with these two.