This paper shows that when a classifier is evaluated with nonrandom test data, ROC curves differ from the ROC curves that would be obtained with a random sample. To address this bias, this paper introduces a procedure for plotting ROC curves that are inferred from nonrandom test data. I provide simulations and an example with wine data to illustrate the procedure as well as the magnitude of bia...

Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation betw...

The Receiver Operating Characteristic (ROC) curve is a useful tool to measure the classification capability of continuous variable assess accuracy medical test that distinguishes between two conditions. Sometimes, covariates related diagnostic may increase discriminating power ROC curve. Due lack stability classical curves estimators outliers, we introduce procedure obtain robust in presence co...