18 ms·
AUC being “area under curve” or something else?
by O__________O 4y ago
AUC being “area under curve” or something else?
- acomms 4y agoCorrect, it is a performance measure for ML algos (https://www.sciencedirect.com/science/article/abs/pii/S0031320396001422 https://www.sciencedirect.com/science/article/abs/pii/S00313...)
- PaulHoule 4y agoYep. See https://en.wikipedia.org/wiki/Receiver_operating_characteristic https://en.wikipedia.org/wiki/Receiver_operating_characteris... If you had to pick one number for judging classifiers that is the one. "Accuracy" is a really bad one because it is not meaningful w/o careful thought about the problem. For instance if you have a diagnostic system for a disease that has a .01 prevalence, you get 99% accuracy if you say nobody has a disease. Practically a person might want to operate a classifier with different thresholds depending on how bothered they are by false positives vs false negatives, the ROC shows what choices are have available with a particular and the AUC of the ROC gives you one number that characterizes the quality of the classifier overall. A system with an AUC under say 0.55 hasn't really learned anything at all, at 0.6 it is showing signs of life, looking at those curves I'd say Tik Tok is beating me solidly. In this case it is limited by the problem being fuzzy (something I thumbs up today might get a thumbs down tomorrow), something up in the 0.95 level could be attained for something like "is this article about the outcome of a sports game?"