As an example I have a confusion matrix that shows good accuracy but poor performance on sensitivity because of imbalanced classes. I made this fictive table for a presentation.
actual actual positive negative pred. positive 6 20 pred. negative 6 986
I have two questions:
First, is it possible to draw a ROC curve that matches with this confusion matrix and how to do this without having the original data? Is there an easy/short way or should I somehow reconstruct some "fictive" data.
Second, could different ROC curves potentially match with the same confusion matrix? I thought that classification thresholds may differ and therefore may result in different ROC curves with similar confusion matrix (see Fawcett 2006.
For implementation I am using the ROCR package in R.