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.

翻译：本文研究了用于评估机器学习模型的质量指标的准确性与鲁棒性。机器学习模型的预测质量通过一种基于交叉验证的方法进行模型无关的评估，该方法可对未知数据的近似误差进行估计。所提出的指标量化了模型预测中可解释的变异量。这些指标的可靠性通过多个数值算例进行评估，其中存在可用于验证估计预测误差的额外数据集。此外，本文估算了所提质量指标的置信界限，并通过交叉验证方法获得的预测残差推导出局部质量指标。