I am extracting image features from 10 classes with 1000 images each. Since there are 50 features that I can extract, I am thinking of finding the best feature combination t
kNN is not trained. All of the data is kept and used at run-time for prediction, so it is one of the most time and space consuming classification method. Feature reduction can reduce these problems. Cross validation is a much better way of testing then train/test split.