I do not understand this relationship.
According to wikipedia, the CER can easily be obtained from the ROC curve.
Equal error rate or crossover error rate (EER or CER): the rate at which both acceptance and rejection errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is the most accurate.
However, the ROC and CER curves are clearly very different: ROC
How is the Cross Over Error rate "easily obtained" from the ROC curve? Or rather, what is the quantitative relationship between these?
It might be worth mentioning that my application is not for biometrics, but it is a binary classifier. Thanks