[DanAndersen]

This is true, which is why machine learning has long since learned to not even think of what you describe as a meaningful measure of accuracy. If you look at the linked paper [0], you'll find that the author uses the "ROC AUC" metric [1]:

>The ROC AUC score represents the probability that when given one randomly chosen positive instance and one randomly chosen negative instance, the classifier will correctly identify the positive instance

[0] https://arxiv.org/pdf/1902.10739.pdf [1] https://en.wikipedia.org/wiki/Receiver_operating_characteris...

[rahimnathwani]

Thanks. That makes more sense.

The article didn't mention AUC, so I assumed they were talking about accuracy in the sense people normally mean it, which also matches the definition in the sidebar of the wikipedia link you shared:

(TP + TN) / (P + N)