The ROC curve is a graphical tool for the evaluation and comparison of binary classifiers. It provides more complete evaluation than the confusion matrix and the error rate. It is valid even if we deal with a non-representative test set i.e. the observed class frequencies are not an estimate of the prior class probabilities. It is especially useful when we deal with class imbalance, and when the misclassification costs matrix is not well established.
In these slides, we show: the ideas underlying the ROC curve; the construction of the curve from a dataset; the calculation of the AUC (area under curve), a synthetic indicator derived from the ROC curve; and the use of the ROC curve for model comparison.
Keywords: receiver operating characteristic, roc curve, auc, area under curve, binary classifier, evaluation, model comparison, class probability estimate, score
Components (Tanagra): SCORING, ROC CURVE
Slides: ROC curve
References:
Wikipedia, "Receiver Operating Characteristic".
T. Fawcett, "An introduction to ROC analysis", Pattern Recognition Letters, 27, 861-874, 2009.
