Solved – Why is there a sharp elbow in the ROC curves

discriminant analysisrroc

I have some EEG data sets that I am testing against two classes. I can get a decent error rate from LDA (the class-conditional distributions aren't Gaussian, but have similar tails and good enough separation), and so I want to plot the ROC of the LDA predictor against data sets from other subjects.

Here is a typical graph for the predictor tested against a single trial:
enter image description here

I have tried a couple of different packages (pROC and ROCR), and the results are consistent. My question is, what's with the sharp elbow? Is it just an artifact of the projection produced by the LDA, i.e., there happens to be a 'cliff' where the classifier performance plummets?

Best Answer

A perfect ROC "curve" will be shaped with a sharp bend. The performance you have there is very near perfect separation. In addition, it looks like you have a scarcity of points making the curve.

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