I have never seen tabular transformer models beat xgboost/catboost in industrial context where datasets is gigantic. They most produce these results on relatively small datasets, clearly not in the tens of millions of rows.
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
I would love to point them at my old time series prediction problems to see their ability. I was very proud of my lightgbm back then, performing miracles after a heck of a lot fearure engineering.
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.
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
Why "Every number is a measured one" and not "every parameter is measured?"
Stopped reading at that point.
LLMs are like autotune. Imagine the Rolling Stones auto-tuned.
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.