For classification tasks, besides AUC values, additional parameters depending on the amount of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) were investigated: sensitivity / recall (Sens, eq. 4), specificity (Spec, eq. 5), positive predictive value / precision (PPV, eq. 6), negative predictive value (NPV, eq. 6), balanced accuracy (Acc, eq. 8) and the F1 score (eq. 9). Furthermore, the overall best models were determined based on the normalized Matthew´s correlation coefficient (nMCC, eq. 10). Unlike Acc and F1 metrics, this statistic includes all four elements of a confusion matrix and is considered more reliable for binary classification tasks .