Dear Danny,

are you sure that the area under the ROC curve is 0. For me that does not make any sense, as non-informative (pure random) model achieves an area of 0.5? I would propose that you re-check those computations.

Best

Matthias

A random classifier for a balanced set will give 0.5 AUC.

An area of 0 dos not make sense, unless you have an unbalanced dataset (and true probability of one class is 0.9+), and your classifier is putting everything to the highly improbable class.

In that case, simply reversing your classifier prediction should result in AUC~1

Dear Danny,

on how many observations the model was built?

Ad.: point 1 and 2 provide mostly close to univocal information, which suggests to ask in ad. to point 3: do you really need this type of model? Accuracy of 64% is not that bad, but somehow does it really match the r2=~0.08 (r=0.28). 64% accuracy would suggest that the model describes more that 8% of data it was built on.

If my understanding of your description is right, there should be some error during calculations somewhere.

Point 4: For accuracy 64% ROC cannot be 0.--> how many data samples were used to build ROC curve? --> how many observations were used to build and validate the model?

Regards,

Richard Gomolka

Danny it seems that there is some problem in this analysis, instead.