Solved – Area under curve of ROC vs. overall accuracy

classificationroc

I am a little bit confused about the Area Under Curve (AUC) of ROC and the overall accuracy.

  1. Will the AUC be proportional to the overall accuracy? In other words, when we have a larger overall accuracy will we definitely a get larger AUC? Or are they by definition positively correlated?

  2. If they are positively correlated, why do we bother reporting both of them in some publications?

  3. In real case, I performed some classification task and got the results as follows: classifier A got an accuracy 85% and AUC of 0.98 and classifier B got an accuracy of 93% and AUC of 0.92. Question is, which classifier is better? Is it possible to get results similar to these (or do my results indicate a bug in my implementation)?

Best Answer

AUC (based on ROC) and overall accuracy seems not the same concept.

Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity. So when we compare the overall accuracy, we are comparing the accuracy based on some cutpoint. The overall accuracy varies from different cutpoint.

Related Question