TabPFN and TabICL vs. tuned XGBoost: the model that doesn't train won 14/14

Tabular foundation models are one of those things where when you first look into it, it does not make sense as to why they would work so well but it does.

In drug property prediction domain, tabular foundation models coupled with another foundation model for molecules are pretty close to being the state of art.

Btw the article makes heavy use of AI or is written in that way A lot of unnecessary dramatic flair that gets very tiring

  XGBoost’s search optimized accuracy, and afterwards I also compare by area under the curve. Which means the “fourteen of fourteen on AUC” is against a boosting model that was not tuned for that metric. Tuning it for AUC would probably improve it there; I did not measure that.

So, not a fair test?

I also did not get why Xgboost had to count its training time for the inference. You only train once. I guess in some scenario, where someone says, "I need the best model now, you have five minutes on this singular dataset", but I have never been in that situation.

I would feel better if scripts were released, because I am fairly dubious. I take it as a given that a tabular model has been pre-trained on all of the public benchmark datasets, but that is what it is.

The slop was so meandering, I am not sure what is truth or not.