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Many fields use the ROC curve and the PR curve as standard evaluations ofbinary classification methods. Analysis of ROC and PR, however, often givesmisleading and inflated performance evaluations, especially with an imbalancedground truth. Here, we demonstrate the problems with ROC and PR analysisthrough simulations, and propose the MCC-F1 curve to address these drawbacks.The MCC-F1 curve combines two informative single-threshold metrics, MCC and theF1 score. The MCC-F1 curve more clearly differentiates good and badclassifiers, even with imbalanced ground truths. We also introduce the MCC-F1metric, which provides a single value that integrates many aspects ofclassifier performance across the whole range of classification thresholds.Finally, we provide an R package that plots MCC-F1 curves and calculatesrelated metrics.

The MCC-F1 curve: a performance evaluation technique for binary classification