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# AUC (Area under the ROC Curve)

![Source: Data Science Central](/files/-LvHDdtKiSfM4WORukWK)

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