# 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**. 
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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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![](https://2552912007-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LzGBVuquaFNrwdmJna0%2F-M3tPrmlRWHc4ZvljB5L%2F-M3tRRwFchB_WKbIGBXH%2FScreen%20Shot%202020-04-01%20at%2011.51.16%20PM.png?alt=media\&token=ecfc126f-392a-4455-80c4-b6aa94cc4bb2)

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