> For the complete documentation index, see [llms.txt](https://ai.nuhil.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai.nuhil.net/statistics/roc-and-auc.md).

# ROC and AUC

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**ROC Curve** Is the plot of Sensitivity/TPR (Y axis) vs (1-Specificity)/FPR (X axis). It is a graphical plot that illustrates the diagnostic ability of a binary classifier, as its **discrimination threshold is varied**. 
{% endhint %}

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AUC is a metric for how well a model fit the data (1 being absolutely perfect fit).
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![](/files/-M3tRRwFchB_WKbIGBXH)

{% embed url="<https://www.youtube.com/watch?v=OAl6eAyP-yo>" %}


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