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This question already has an answer here:

I used the sklearn.metric roc_auc_score,it gave me a value 0.91.

What is the does this number mean?

I am interested to learn how this is calculated,could someone please direct me to some information on this?

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marked as duplicate by Sycorax, Michael Chernick, mdewey, Peter Flom Jul 27 '18 at 12:03

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I think this is covered in Wikipedia and many other similar posts here. Please look at the comments.

AUC of 0.91 is much better than a random model (AUC=0.5), but it doesn't mean your model is good. You will need to compare your model with a reference model. If your reference model has AUC 0.95, your AUC 0.91 is bad. However, if your reference AUC is 0.70, then your AUC 0.91 is good.

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  • $\begingroup$ How would you choose a reference model? $\endgroup$ – Nikolas Rieble Dec 20 '16 at 9:14
  • $\begingroup$ @NikolasRieble That depends on your problem. Specificity, sensitivity? $\endgroup$ – SmallChess Dec 20 '16 at 9:47
  • $\begingroup$ By a reference model do you simply mean another measurement of accuracy/ quality (Specificity, sensitivity)? Or do you mean actually another modell (such as SVM) to compare both performances? $\endgroup$ – Nikolas Rieble Dec 20 '16 at 9:51

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