AUC (Area under the ROC Curve)

Source: Data Science Central

AUC is one of the most important evaluation metrics for measuring the performance of any classification model. It is a performance measurement for a classification problem at various thresholds settings.

The ROC Curve measures how accurately the model can distinguish between two things (e.g. determine if the subject of an image is a dog or a cat). AUC measures the entire two-dimensional area underneath the ROC curve. This score gives us a good idea of how well the classifier will perform.

AUC is related to another evaluation metric called the Confusion Matrix.