The receiver operating characteristic curve accurately assesses imbalanced datasets

Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) have been reported as ill-suited to evaluate prediction performance on imbalanced problems where there is more interest in performance on the positive minority class, while the precision-recall (PR) curve is preferable. We show via simulation and a real case study that this is a misinterpretation of the difference between the ROC and PR spaces, showing that the ROC curve is robust to class…

• UD
  U.S. Department of Health and Human Services
• NI
  National Institutes of Health
• NC
  National Cancer Institute

  Award: U24CA248138
• NI
  National Institute of Allergy and Infectious Diseases

  Award: 75N93019C00001