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As a lazy non-ML simpleton, is there a simple explanation for it's usage? Would tabular classification usually refer to say, extraction of tabular data in a picture to text? I tried googling and looking through the site but it wasn't obvious to me what this actually does.
No it’s really just tabular csv data, like a typical spreadsheet would hold. These datasets are rarely ever outperformed by deep learning compared to standard ML.
It's exciting to see a novel approach to applying NNets to Tabular ML. Definitely need to call out the 1000 row limitation. It will be interesting to see if this approach stands the test of time. Other algorithms (SAINT cough, cough) made big claims, but AFAIK no one actually uses them. It's still a "XGBoost is all you need" world in tabular ML (unless you've discovered AutoGluon).
Thanks. Looks very interesting! My main observation just looking at your example pictures is that its closest competitor is Gaussian Processes which I've long been a fan of. Just looking at those pictures it looks like GP and TabPFN are very similar where there is data but TabPFN is more happy to extrapolate while GP is localised around the data (look at the top row for example). I can't decide whether that's a featu…
Yeah, we also find this interesting. Arguably, though, GP is far worse in all metrics. Thus, it is not really our closest competition.
> This may revolutionize data science: we introduce TabPFN, a new tabular data classification method that takes 1 second & yields SOTA performance (better than hyperparameter-optimized gradient boosting in 1h). Current limits: up to 1k data points, 100 features, 10 classes. 1/6
[Faster and more accurate than gradient boosting for tabular data: Catboost, LightGBM, XGBoost]