What is the ROC Curve and what is AUC (a.k.a. AUROC)?

ROC Curve and AUC

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs. False Positive Rate (x-axis).

AUC is the area under the ROC curve, and it’s a common performance metric for evaluating binary classification models.

It’s equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative.


The area under the curve (AUC) can be used as a summary of the model skill when comparing your models’ performance. The higher the AUC the better the model to separate classes.


ROC Curves summarize the trade-off between the true positive rate and false positive rate for a predictive model using different probability thresholds. ROC curves are appropriate when the observations are balanced between each class, whereas precision-recall curves are appropriate for imbalanced datasets.