In this step, we decide on the criterion to distinguish good vs. bad classifiers. For example, the percentage of correct predictions on test set. The learned models will be evaluated on evaluation functions (also called objective functions or scoring functions) in order to distinguish between successful and unsuccessful models. The desired, external evaluation function might be different from the one the algorithm uses.