# 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). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/auc-area-under-the-roc-curve.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.