AUC for someone with no stats knowledge Can someone explain what area under the curve means for someone with absolutely no stats knowledge? For example, if a model claims an AUC of 0.9, does that mean that it makes an accurate prediction 90% of the time?

Following up on the comment from @Nuclear Hoagie, the ROC curve for a model is generated by evaluating classifiers using a sequence of thresholds for declaring positive or negative. The AUC represents the area under the curve over the entire range of possible thresholds. Often, only a restricted range of thresholds is really of interest. When this is the case, AUC may not be the best way to compare models.
