I have recently read this:
" AUC(Area Under Curve) is good for classification problems with a class imbalance. Suppose the task is to detect dementia from speech, and 99% of people don’t have dementia and only 1% do. Then you can submit a classifier that always outputs “no dementia”, and that would achieve 99% accuracy. It would seem like your 99% accurate classifier is pretty good, when in fact it is completely useless. Using AUC scoring, your classifier would score 0.5. "
Can someone please explain why does it reach 0.5? If 99% are negative and we output always "no", wouldn't that mean that the TruePositiveRate will be very high and the FalsePositiveRate very low, resulting in an Area Under Curve close to one?