The correct answer is D. All of the mentioned.
A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The TPR is the proportion of actual positives that are correctly identified as such, while the FPR is the proportion of actual negatives that are incorrectly identified as positives.
A non-parametric receiver operating characteristic (NOC) curve is a generalization of the ROC curve that does not make any assumptions about the underlying distribution of the data. The NOC curve is created by plotting the empirical TPR against the empirical FPR at various threshold settings.
A cumulative operating characteristic (COC) curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The COC curve is created by plotting the cumulative true positive rate (C-TPR) against the cumulative false positive rate (C-FPR) at various threshold settings. The C-TPR is the proportion of all actual positives that are correctly identified as such, while the C-FPR is the proportion of all actual negatives that are incorrectly identified as positives.
All three curves are used to evaluate the performance of a binary classifier system. The ROC curve is the most commonly used curve, but the NOC curve and COC curve can be more useful in some cases. For example, the NOC curve is more robust to outliers than the ROC curve, and the COC curve is more interpretable than the ROC curve.