  One of the main tasks of actuaries and data scientists is to build good
predictive models for certain phenomena such as the claim size or the number of
claims in insurance. These models ideally exploit given feature information to
enhance the accuracy of prediction. This user guide revisits and clarifies
statistical techniques to assess the calibration or adequacy of a model on the
one hand, and to compare and rank different models on the other hand. In doing
so, it emphasises the importance of specifying the prediction target functional
at hand a priori (e.g. the mean or a quantile) and of choosing the scoring
function in model comparison in line with this target functional. Guidance for
the practical choice of the scoring function is provided. Striving to bridge
the gap between science and daily practice in application, it focuses mainly on
the pedagogical presentation of existing results and of best practice. The
results are accompanied and illustrated by two real data case studies on
workers' compensation and customer churn.
