  In this paper the accuracy and robustness of quality measures for the
assessment of machine learning models are investigated. The prediction quality
of a machine learning model is evaluated model-independent based on a
cross-validation approach, where the approximation error is estimated for
unknown data. The presented measures quantify the amount of explained variation
in the model prediction. The reliability of these measures is assessed by means
of several numerical examples, where an additional data set for the
verification of the estimated prediction error is available. Furthermore, the
confidence bounds of the presented quality measures are estimated and local
quality measures are derived from the prediction residuals obtained by the
cross-validation approach.
