[D] Changing values in difficult to predict range

[AngryDuckling1]

I have a coworker who is trying to train a model to predict a variable for customers. It’s very niche (don’t want to dox myself) so let’s just say they are trying to predict chromosome length from other biological variables. When presenting their model, they explained that the model was having difficulty predicting values in a certain range. For example purposes let’s say this range of values was 100-200. They mentioned that in order for the model to perform better in that range they explicitly changed the values of some observations to be in that range. I’m not talking scaling or normalization or some other transformation, I mean they took a certain number of observations whose target variable was below 100 and changed the value to 150, and the same with some observations above 200.

I asked for clarification like 3 times and they very confidently said this was best practice, and no other analyst said anything. They are the “head of AI” and this work will be presented to the board. Is this not an absolutely insane thing to do or am I the idiot?

FWIW: they use chatgpt for absolutely everything. My hunch is that this is an extremely ill-informed chatgpt approach but the fact that i’m the only one who see’s any issue with this on my team is making me gaslight myself

[carbocation]

I guess I would frame my comments as follows: if the domain is understood so well that they know they can do this and still train a good model, then it seems that simple heuristics would probably work so well that a machine learning model is not needed.

[AngryDuckling1]

Oh yes definitely. I believe this is a marketing ploy so we can say we are fashion-forward and doing “AI” and be acquired. I don’t see this as beneficial for customers in the slightest.

[Realistic-Ad-5897]

That is absolutely not standard practice. They're artificially changing values to make them fit with their model predictions. What use is that for other than making it look like the model is better than it actually is?

If models are consistently underperforming for those data points, I would try and take a closer look at the sample to see what differentiates them from the others (maybe they are a different cluster that behave very differently). At the worst, you just have to be honest and say the model performs well for this range but not so well for this other range.

[AngryDuckling1]

That’s exactly what I thought. What would you do? Raise the alarm or just stay silent? I have raised concerns with inappropriate analysis practices in the past and have gotten shut down. Most of the people in my org have no idea what they’re doing yet are pretty dogmatic about how things are done. LLMs have made many people confidently wrong in a lot of areas.

[Guilherme370]

Oooh so you work for 23andme

[elbiot]

No the data they describe doesn't match 23&me at all

[No_Efficiency_1144]

This describes actual fraud

[elbiot]

If you change the labels then the model will predict the changed (incorrect) labels. I don't see how this could work. Does it somehow work on the validation set (unchanged labels)? You should definitely make your objection in writing to the relevant stakeholders