ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules

arXiv:2603.29928v4 Announce Type: replace Abstract: Tabular foundation models such as TabPFN, TabICL, ExaOneTabular, and Nori already produce full predictive distributions, yet prevailing regression benchmarks evaluate them almost exclusively via point-estimate metrics such as RMSE. This overlooks the distributional information these models are designed to provide. We introduce ScoringBench, an open and extensible benchmark that evaluates tabular regression models under a comprehensive suite of proper scoring rules - including (weighted) CRPS, energy scores and interval scores - alongside standard point metrics. ScoringBench covers 101 regression datasets from diverse domains, supports transparent community contributions via a git-based leaderboard, and provides two complementary ranking protocols: an ordinal Demsar/autorank approach and a magnitude-preserving $z$-score ranking approach. Our evaluation of various model types, including (fine-tuned) tabular foundation models, gradient-boosted trees, and MLPs, shows that model rankings shift depending on the chosen scoring rule. Models that perform well on point-estimate metrics can rank lower on probabilistic ones. These findings demonstrate that evaluation metric selection is a core modeling decision and a domain-specific requirement for applications where certain types of errors carry higher consequences.

Abstract:Tabular foundation models such as TabPFN, TabICL, ExaOneTabular, and Nori already produce full predictive distributions, yet prevailing regression benchmarks evaluate them almost exclusively via point-estimate metrics such as RMSE. This overlooks the distributional information these models are designed to provide. We introduce ScoringBench, an open and extensible benchmark that evaluates tabular regression models under a comprehensive suite of proper scoring rules - including (weighted) CRPS, energy scores and interval scores - alongside standard point metrics. ScoringBench covers 101 regression datasets from diverse domains, supports transparent community contributions via a git-based leaderboard, and provides two complementary ranking protocols: an ordinal Demsar/autorank approach and a magnitude-preserving $z$-score ranking approach. Our evaluation of various model types, including (fine-tuned) tabular foundation models, gradient-boosted trees, and MLPs, shows that model rankings shift depending on the chosen scoring rule. Models that perform well on point-estimate metrics can rank lower on probabilistic ones. These findings demonstrate that evaluation metric selection is a core modeling decision and a domain-specific requirement for applications where certain types of errors carry higher consequences.

Artificial Intelligence (cs.AI)

arXiv:2603.29928 [cs.AI]

(or arXiv:2603.29928v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.29928 Focus to learn more arXiv-issued DOI via DataCite

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ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules

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