The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under...

Abstract The receiver operating characteristic (ROC) curve is defined as true positive rate versus false positive rate obtained by varying a decision threshold criterion. It has been widely used in medical science for its ability to measure the accuracy of diagnostic or prognostic tests. Mathematically speaking, ROC curve is the composition of survival function of one population with the quanti...

The performance of a classifier can be improved by abstaining on uncertain instance classifications. The transformation from the original Receiver Operator Characteristic (ROC) curve to the curve obtained by abstention is provided. We include proofs on dominance of this new ROC curve to aid classifier selection and to show the effectiveness of the approach. For specific cost and class distribut...

Evaluation of a recommender system algorithm is a challenging task due to the many possible scenarios in which such systems may be deployed. We have designed a new performance plot called the CROC curve with an associated statistic: the area under the curve. Our CROC curve supplements the widely used ROC curve in recommender system evaluation by discovering performance characteristics that stan...

Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three different ways of using covariate inf...

The purpose of this tutorial-based lecture is to show the usefulness of performing a receiver operating characteristic (ROC) curve analysis. The lecture will explain the background terminology associated with a ROC curve analysis, show the necessary SAS software coding to run a ROC curve analysis, and finally help interpret the analysis output in order to make informed research decisions. An ex...