Ive tried to understand what the ROC Curve represents and what EER (Equal Error Rate) means. And I somehow think I got to understand some of the explanations I read on the internet and videos I watched and papers I read. But I still cant get a grip on what it means in my particular case.
I am reading a paper on face verification. The described method is trained and tested on the "Labeled Faces in the Wild"-Benchmark. That benchmark offers a standard dataset for comparison to other face-verification-methods. It essentually is a huge database of image-pairs which each depict the same or different persons. The methods at hand should now tell if the depicted persons on these image-pairs are the same or different people. The machine learned method is evaluated with a measure called ROC-EER and the score/result is stated in percentage. And they also evaluated by doing a 10-fold cross validation (which I actually do understand and know what is being done and why)
For example: ROC-EER, %: 89.5
So I just dont know what that 89% should tell me? 89% of what is what? And how does that value correlates to the 10-fold cross validation procedure?
And I even dont know If I am missing some information for you to answer a complete question…I feel a bit lost here 🙂
Best Answer
The paper you link explains ROC-EER as
So the EER is a way to fix the working point (= threshold for deciding the class) for an actual classifier on the ROC.
So the 89% you quote are then the overall accuracy if you fix the threshold according to the ROC-EER method.
Cross validation is the method ("measurement schedule") how they measured the 89%