ROC curves are often used to display the predictive performance of binary classifiers. The area under the ROC curve (AUC) is a way to compare various classifiers. A perfect classifier has an AUC of 1 and a completely bogus (random) classifier has an AUC of 0.5. You can read more about ROC curves here.There is a ton of code for plotting ROC curves and calculating AUC. But most use 'for' loops. And as we all know, loops slow everything down in MATLAB. You can download my vectorized code for plotting multiple ROC curves from multiple classifiers and calculating AUC curves for each.