7 Things You Should Know about ROC AUC

Models for different classification problems can be fitted by trying to maximize or minimize various performance measures. Measurements that address one aspect of a model's performance but not another are important to note so that we can make an informed decision and select the performance measures that best fit our design. ROC AUC is commonly used in many fields as a prominent measure to evaluate classifier performance, and researchers might favor one classifier over another due to a higher AUC. For a refresher on ROC AUC, a clear and concise explanation can be found here. If you are totally unfamiliar with ROC AUC you may find that this post digs into the subject a bit too deep, but I hope you will still find it useful or bookmark it for future reference.