What is the Area Under the ROC Curve in Machine Learning?

The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) is a measure of the performance of a binary classifier system as its discrimination threshold is varied. It is a measure of the ability of the classifier to distinguish between positive and negative classes. The larger the area under the ROC curve, the better the classifier is at distinguishing between positive and negative classes. The AUC is also known as the “area under the curve” (AUC) or “AUC score”. It is a measure of the accuracy of a classifier system, used in machine learning and statistics. The AUC is the probability that the classifier will assign a higher score to a randomly chosen positive example than to a randomly chosen negative example. AUC can be used to compare different classifiers and to determine which one performs better.