[N] (In)validating published ML performance scores is possible

I think many of us came across publications with ML performance scores (such as accuracy, sensitivity, etc.) that seemed unrealistic, still getting a lot of citations. We knew they were false, invalid, incorrect, maybe a typo, maybe some bug in the evaluation, maybe cheating, but no one invested the time to reimplement the method and prove it wrong.

However, ML performance scores - especially if multiple ones are reported - cannot take any values independently. There are numerous constraints imposed …

accuracy cheating citations etc evaluation false machinelearning performance publications sensitivity think