We just put out a blog post with TabPFN benchmarks on datasets from 1M to 10M rows.<\/p>\n
For context: TabPFN is a transformer pretrained on millions of synthetic datasets that does in-context learning for tabular classification\/regression. No hyperparameter tuning needed – you just give it training data at inference and it predicts.<\/p>\n
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- TabPFNv2 published in Nature<\/a> this year<\/li>\n
- TabPFN-2.5 beats models tuned for 4h (report here<\/a>), #1 on TabArena<\/a> leaderboard atm<\/li>\n<\/ul>\n
Compared our Scaling Mode against CatBoost, XGBoost, LightGBM on internal classification datasets. Performance keeps improving with more data and the gap to gradient boosting isn’t shrinking.<\/p>\n
Benchmark results show normalized scores across datasets plus individual results showing ROC AUC improvements. You can find them here: https:\/\/priorlabs.ai\/technical-reports\/large-data-model<\/a><\/p>\n
Would be interesting to keep on benchmarking this on public large tabular datasets. Anyone know good large public tabular datasets?<\/p>\n<\/div>\n
submitted by \/u\/Diligent_Inside6746 <\/a>
[link]<\/a><\/span> [comments]<\/a><\/span><\/p> - TabPFN-2.5 beats models tuned for 4h (report here<\/a>), #1 on TabArena<\/a> leaderboard atm<\/li>\n<\/ul>\n
