In this chapter, the authors present measures that are useful for the evaluation of the overall performance of a (predictive) model. They distinguish between the explanatory and predictive approaches to statistical modelling. The measures may be applied for several purposes, including: model evaluation, model comparison and out-of-sample and out-of-time comparisons. Most model-performance measures are based on the comparison of the model’s predictions with the values of the dependent variable in a dataset. In many situations, consequences of a prediction error depend on the form of the error. There are many more measures aimed at measuring the performance of a predictive model for a binary dependent variable. All model-performance measures presented in the chapter are subject to some limitations. The package covers the most often used measures and methods presented in the chapter.