Whilst the size and complexity of ML mod- els have rapidly and significantly increased over the past decade, the methods for assess- ing their performance have not kept pace. In particular, among the many potential per- formance metrics, the ML community stub- bornly continues to use (a) the area under the receiver operating characteristic curve (AUROC) for a validation and test cohort (distinct from training data) or (b) the sensitivity and specificity for the test data at an optimal threshold determined from the validation ROC.