# AUC (Area under the ROC Curve)

![Source: Data Science Central](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-LvGspxW3Zko2589SZEN%2F-LvHDdtKiSfM4WORukWK%2Fimage.png?alt=media\&token=37e218e6-2fe1-4e71-8a69-bdf47bf117e0)

AUC is one of the most important [evaluation metrics](https://machine-learning.paperspace.com/wiki/metrics-in-machine-learning) 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](https://machine-learning.paperspace.com/wiki/confusion-matrix).