Points of Significance: Classification evaluation

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Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Likewise, auROC evaluates the average recall across all false positive rate (FPR) thresholds, which is often a highly misleading metric in class-imbalanced domains [71,328].”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “s are, as are other machine learning methods, prone to error if not grounded in computational, statistical, and experimental expertise [6], with proper controls and performance metrics being critical [26]. For those seeking extensive reviews, there are a number of existing deep learning application-”
Predicting diabetes-related hospitalizations based on electronic health records (2018, background): “It is worth noting that unlike the ROC curve, the PRC is not guaranteed to be monotonic.(33) While ROC curves could provide misleading interpretation of specificity when utilized in imbalanced classification cases,(34,35) (i.”