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?
AUC is difficult to understand and interpret even with statistical knowledge. Without such knowledge I’d stick to the following stylized facts:
- AUC close to 0.5 means a model performance wasn’t better than randomly classifying subjects. It wasn’t better than a silly random number generator to mark the samples as positive and negative.
- AUC is used by some to compare models.
- Higher AUC suggests better demonstrated performance in classification.
- AUC is a noisy metric
- Max AUC is 1, for a classification model that is never wrong
- Although technically Min AUC is 0, it makes little sense to have AUC lesser than 0.5. AUC zero means that by a simple switch from positive to negative label you get to a perfect classification