TabPFN and TabICL against tuned XGBoost: the model that does not train won on fourteen tables out of fourteen

The claim behind TabPFN and TabICL: they predict on a table without ever training on it, and still beat tuned boosting.

• Measured on 14 datasets from the Grinsztajn benchmark, same split and same clock for everyone.
• The model that does not train won on 14 out of 14 against tuned XGBoost.
• What it costs in latency and VRAM, and when I'd still reach for boosting — in the post.

Read the full measurements → https://efraingaray.com/en/blog/tabpfn-vs-xgboost/

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