4 ms·
> They claim that instead of a real-world accuracy, they wanted to find the “max” accuracy that their classifier was statistically capable of Yeah, I read this
by pedrosorio 3y ago
> They claim that instead of a real-world accuracy, they wanted to find the “max” accuracy that their classifier was statistically capable of
Yeah, I read this on the GitHub issue a week ago and couldn't believe it. Ideally, their profile(1) should allow them to quickly admit they were wrong on such a simple issue. Pursuit of truth and knowledge, etc.
(1) a young PhD from a prestigious university
> For example, I think a neural network is capable of achieving a “max” accuracy of 100%
Why reach for such powerful tools? f(x) = random(num_classes), achieves 100% "upper bound" accuracy.
- civilized 3y agoIf I was confronted with this kind of nonsense in my data science job, I would lose all respect for the person who produced it and never thereafter trust anything they said without thoroughly vetting it. There's only two options here, deceptive or hopelessly incompetent.
- microtonal 3y agoIdeally, their profile(1) should allow them to quickly admit they were wrong on such a simple issue. Academia doesn't have a culture of admitting mistakes. Retracting a paper is typically seen as something shameful rather than progress (by scrutinizing results). Combined with pressure to publish and sometimes limited engineering skills it leads to a volatile mix. There are a lot of published results that are not reproducible when you'd try (not saying anything new here, see replication crisis).
- dekhn 3y agoIn its native environment, the scientists reserves its fiercest attacks for its competitors: by fighting tooth and nail, it can render its environment uninhabitable for nearly all but the most determined adversary. Sometimes, this ensures access to desirable mates, but not always.