ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules
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 le...
| Authors | Jonas Landsgesell, Pascal Knoll, Tizian Wenzel |
|---|---|
| Primary category | cs.AI |
| Published | 31 March 2026 |
| Last updated at source | 12 October 2026 |
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