  The paper proposes new second-order accuracy metrics for scoring or rating
models, which show the target preference of the model, it is better to diagnose
good objects or better to diagnose bad ones for a constant generally accepted
predictive power determined by the first order metric that is known as the Gini
index. There are two metrics, they have both an integral representation and a
numerical one. The numerical representation of metrics is of two types, the
first of which is based on binary events to evaluate the model, the second on
the default probability given by the model. Comparison of the results of
calculating the metrics allows you to validate the calibration settings of the
scoring or rating model and reveals its distortions. The article provides
examples of calculating second-order accuracy metrics for ratings of several
rating agencies, as well as for the well known approach to calibration based on
van der Burg's ROC curves.
