Yes, you overfit the training data set, so you "under-fit" the test set. I'm trying to suggest why more degrees of freedom (features) makes for a "worse" fit. It doesn't, on the training set, but those same parameters may fit the test set increasingly badly.
It doesn't make sense to evaluate on a training set. On Thu, May 9, 2013 at 3:21 PM, Gabor Bernat <ber...@primeranks.net> wrote: > Yes, but overfitting is for train dataset isn't it? However, now I'm > evaluating on a test dataset (which is sampled from the whole dataset, but > that still makes it test), so don't really understand how can overfitting > become an issue. :-? > > Is there any class/function to make the evaluation on the train dataset > instead?
