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?


AUC is difficult to understand and interpret even with statistical knowledge. Without such knowledge I’d stick to the following stylized facts:

  1. 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.
  2. AUC is used by some to compare models.
  3. Higher AUC suggests better demonstrated performance in classification.
  4. AUC is a noisy metric
  5. Max AUC is 1, for a classification model that is never wrong
  6. 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

Source : Link , Question Author : Forest , Answer Author : Aksakal

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