  Many fields use the ROC curve and the PR curve as standard evaluations of
binary classification methods. Analysis of ROC and PR, however, often gives
misleading and inflated performance evaluations, especially with an imbalanced
ground truth. Here, we demonstrate the problems with ROC and PR analysis
through simulations, and propose the MCC-F1 curve to address these drawbacks.
The MCC-F1 curve combines two informative single-threshold metrics, MCC and the
F1 score. The MCC-F1 curve more clearly differentiates good and bad
classifiers, even with imbalanced ground truths. We also introduce the MCC-F1
metric, which provides a single value that integrates many aspects of
classifier performance across the whole range of classification thresholds.
Finally, we provide an R package that plots MCC-F1 curves and calculates
related metrics.
