Straight line in roc curve 
Hi, what does the green straight line at the end of the plot indicate? Is there some problem with the model?
 A: Possibility 1 (likely)
When you increase the sensitivity you can do this in small steps until (apparently) a true positive rate of about 65%. After that, the model makes a big jump to 100%. This can occur for instance when 35% of your positive cases have all the same value (e.g. zero).
The straight line occurs because the way that you plot. Your model is computed for points, but you draw a line in between them.
This straight line is not wrong though. If you want to have a positive rate in between 65% and 100% then you can do this by mixing the models with sensitivity of 65% and 100%. (See: Combining classifiers by flipping a coin)
Possibility 2 (not very likely but theoretically possible)
Theoretically, this might also occur if the positive and negative populations have the same distribution density (with only a difference in a constant) in the region where the predictor parameter is set for high sensitivity.
But, it would be unlikely to get such a straight line due to statistical fluctuations in the sample (unless the curve is a theoretical computation). However, in the special case that all the values are all the same (as in the first case), eg. zero, then you don't get statistical fluctuations.
Possibility 3
It might be that you don't compute the entire ROC curve, but the front-end that plots the curve automatically and completes the curve by connecting the end of your computed curve with the point 1,1
