# ROC curve

> A graph of true positive rate versus false positive rate for different classification thresholds in binary classification. The shape of an ROC curve suggests a binary classification model's ability to separate positive classes from negative classes.[^1]

A graph of [true positive rate](https://wiki.g15e.com/pages/Recall%20(machine%20learning.txt)) versus [false positive rate](https://wiki.g15e.com/pages/False%20positive%20rate.txt) for different [classification thresholds](https://wiki.g15e.com/pages/Classification%20threshold.txt) in [binary classification](https://wiki.g15e.com/pages/Binary%20classification.txt). The shape of an ROC curve suggests a binary classification model's ability to separate positive classes from negative classes.[^1]

[Classification threshold](https://wiki.g15e.com/pages/Classification%20threshold.txt)가 1.0에서 0.0으로(좌에서 우로) 바뀜에 따라 TPR과 FPR이 어떻게 변하는지 보여주는 시각화 방식.

![ROC](https://developers.google.com/static/machine-learning/crash-course/images/auc_abc.png)

## Etymology

Receiver operating characteristic, is a holdover from <World War II> radar detection.[^2]

## See also

- [AUC](https://wiki.g15e.com/pages/Area%20under%20the%20ROC%20curve.txt): A numerical metric that summarizes the ROC curve into a single floating-point value.

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#ROC
[^2]: [ML crash course - Classification](https://wiki.g15e.com/pages/ML%20crash%20course%20-%20Classification.txt)