ROC and AUC with a Binary Predictor: a Potentially Misleading Metric.

ABSTRACT: In analysis of binary outcomes, the receiver operator characteristic (ROC) curve is heavily used to show the performance of a model or algorithm. The ROC curve is informative about the performance over a series of thresholds and can be summarized by the area under the curve (AUC), a single number. When a predictor is categorical, the ROC curve has one less than number of categories as potential thresholds; when the predictor is binary there is only one threshold. As the AUC may be used in decision-making processes on determining the best model, it important to discuss how it agrees with the intuition from the ROC curve. We discuss how the interpolation of the curve between thresholds with binary predictors can largely change the AUC. Overall, we show using a linear interpolation fro

SUBMITTER: Muschelli J 

PROVIDER: S-EPMC7695228 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature