Posted on Sep 28 Originally published at efraingaray.com
#machinelearning #benchmarks #datascience #python
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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