<P> An application of this process is in early stopping, where the candidate models are successive iterations of the same network, and training stops when the error on the validation set grows, choosing the previous model (the one with minimum error). </P> <P> A test dataset is a dataset that is independent of the training dataset, but that follows the same probability distribution as the training dataset . If a model fit to the training dataset also fits the test dataset well, minimal overfitting has taken place (see figure below). A better fitting of the training dataset as opposed to the test dataset usually points to overfitting . </P> <P> A test set is therefore a set of examples used only to assess the performance (i.e. generalization) of a fully specified classifier . </P> <P> Most simply, part of the original dataset can be set aside and used as a test set: this is known as the holdout method . </P>

Difference between training data set and test dataset