Comparing Model Evaluation Techniques Part 1: Statistical Tools & Tests - DataScienceCentral.com

Evaluating a model is just as important as creating the model in the first place. Even if you use the most statistically sound tools to create your model, the end result may not be what you expected. Which metric you use to test your model depends on the type of data you're working with and your comfort level with statistics. The following summary of model evaluation techniques is by no means exhaustive; it's intended to be a starting point if you're unfamiliar with the available techniques. In part 1, I discuss some of the common Statistical Tools and Tests.