3 ms·
> although later investigation suggests there may have been data leakage I think this point is often forgotten. Everyone should assume data leakage until it is
by godelski 1y ago
> although later investigation suggests there may have been data leakage
I think this point is often forgotten. Everyone should assume data leakage until it is strongly evidenced otherwise. It is not on the reader/skeptic to prove that there is data leakage, it is the authors who have the burden of proof.
It is easy to have data leakage on small datasets. Datasets where you can look at everything. Data leakage is really easy to introduce and you often do it unknowingly. Subtle things easily spoil data.
Now, we're talking about gigantic datasets where there's no chance anyone can manually look through it all. We know the filter methods are imperfect, so it how do we come to believe that there is no leakage? You can say you filtered it, but you cannot say there's no leakage.
Beyond that, we are constantly finding spoilage in the datasets we do have access to. So there's frequent evidence that it is happening.
So why do we continue to assume there's no spoilage? Hype? Honestly, it just sounds like a lie we tell ourselves because we want to believe. But we can't fix these problems if we lie to ourselves about them.
- antithesizer 1y agoThe supposed location of the burden of proof is really not the definitive guide to what you ought to believe that people online seem to think it is.
- mathgeek 1y agoCan you elaborate? You've made a claim, but I really think there'd be value in continuing to what you actually mean.
- NormLolBob 1y agoThey mean “vet your sources and don’t blindly follow the internet hive-mind.” or similar; burden of proof is not what the internet thinks. Tacked their actual point on to the end of a copy paste of op comments context, ended up writing something barely grammatically correct. In doing so they prove why exactly not to listen to the internet. So they have that going for them.
- tbrownaw 1y agoWhat is the relevance of this generic statement to the discussion at hand?
- SamuelAdams 1y agoEvery system has problems. The better question is: what is the acceptable threshold? For an example Medicare and Medicade had a fraud rate of 7.66%. Yes, that is a lot of billions, and there is room for improvement, but that doesn’t mean the entire system is failing: 93% of cases are being covered as intended. The same could be said with these models. If the spoilage rate is 10%, does that mean the whole system is bad? Or is it at a tolerable threshold? [1]: https://www.cms.gov/newsroom/fact-sheets/fiscal-year-2024-improper-payments-fact-sheet https://www.cms.gov/newsroom/fact-sheets/fiscal-year-2024-im...
- fastaguy88 1y agoIn the protein annotation world, which is largely driven by inferring common ancestry between a protein of unknown function and one of known function, common error thresholds range from FDR of 0.001 to 10^-6. Even a 1% error rate would be considered abysmal. This is in part because it is trivial to get 95% accuracy in prediction; the challenging problem is to get some large fraction of the non-trivial 5% correct. "Acceptable" thresholds are problem specific. For AI to make a meaningful contribution to protein function prediction, it must do substantially better than current methods, not just better than some arbitrary threshold.
- sieabahlpark 1y ago[dead]
- mike_hearn 1y agoI think it's worth being highly skeptical about fraud rates that are stated to two decimal places of precision. Fraud is by design hard to accurately detect. It would be more accurate to say, Medicare decides 7.66% of its cases are fraudulent according to its own policies and procedures, which are likely conservative, and cannot take into account undetected fraud. The true rate is likely higher, perhaps much higher. There's also the problem of false negatives vs positives. If your goal is to cover 100% of true cases you can achieve that easily by just never denying a claim. That would of course yield stratospheric false positive rates (fraud). You have to understand both the FN rate (cost of missed fraud) vs the FP rate (cost of fraud fighting) and then balance them. The same applies with using models in science to make predictions.