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Oct 14, 2021 at 19:51 comment added Acccumulation @MichaelLugo If your AUC is less than 0.5, then you should pursue the Constanza method.
Oct 14, 2021 at 0:00 comment added Hasse1987 @Aksakal The probabilistic interpretation is standard. It follows from a simple change of variable.
Oct 13, 2021 at 21:09 comment added user215517 @Aksakal Perhaps we're reading things differently, but from the question linked to: "The second [interpretation] is that the AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example, i.e. P(score(x+)>score(x−))." which is what I was (hoping I was) saying above. "90% of the time" has a standard frequentist interpretation here. The answer that says "AUC is proportional to the number of correctly ordered pairs" is saying the same thing. The proportion of correctly ordered pairs is the AUC.
Oct 13, 2021 at 20:51 comment added Aksakal @user215517 the answer talks about AUC being proportional. Also when constructing ROC you run through all threshold, so the meaning of “90% of time” needs to be defined
Oct 13, 2021 at 20:44 comment added user215517 @Aksakal A proof, more complex than was asked for, of this relationship is given here
Oct 13, 2021 at 19:11 comment added Aksakal Are you sure you’re not talking about 1-specificity? AUC is difficult to pin to ratios and percentages
Oct 13, 2021 at 14:30 comment added Michael Lugo Came here to give this answer. Sure, it's an area under a curve, but you don't need to understand that curve to give this explanation. I'd only add that the scale of AUC is practically from 0.5 to 1 - if you can't even get a 50% chance then your model is worse than random guessing.
Oct 13, 2021 at 5:31 history answered user215517 CC BY-SA 4.0