Fig. 3.

Performance curves for the two binary classifiers that achieved above-chance balanced accuracies – that is, CHR vs. REM (blue curve) and IMP vs. REM (red curve). (A) receiver-operating characteristic (ROC) curves, illustrating the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) across the entire range of detection thresholds, (B) precision-recall (PR) curves, illustrating the trade-off between the precision (positive predictive value) and recall (true positive rate) for different thresholds, and (C) accuracy-reject curves, representing the accuracy of a classifier as a function of the rejection rate (Nadeem et al., 2010). For a comprehensive summary of all classification results, see Table 2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)