An ROC graph depicts relative trade-offs between true positives rate and false positives rate. It needs continuous output of classifier, an estimate of an instance’s class membership probabilities. In fact, a “score”, a numeric value that represents the degree to which an instance is a member of a class is sufficient.

AUC (Area Under Curve) reduces ROC performances to a single scalar value, which enables to compare several classifiers: this area is equivalent to the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance.

In this tutorial, we compare linear discriminant analysis (LDA) and support vector machine (SVM) on a heart-diseases detection problem.

**Keywords**: roc curve, roc graphs, auc, area under curve, classifier performance comparison, linear discriminant analysis, svm, support vector machine, scoring

**Components**: Sampling, 0_1_Binarize, Supervised Learning, Scoring, Roc curve, SVM, Linear discriminant analysis

**Tutorial**: en_Tanagra_Roc_Curve.pdf

**Dataset**: dr_heart.bdm

**References**:

T. Fawcet – « ROC Graphs : Notes and Practical Considerations of Researchers »

Wikipedia - "Receiver operating characteristic"