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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.06814 |
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| _version_ | 1866913015421468672 |
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| author | Jiang, Dihong Cao, Ruoqi Dang, Zhiyuan Huang, Li Zhang, Qingsong Wang, Zhiyu Piao, Shihao Zhu, Shenggao Chang, Jianlong Lin, Zhouchen Tian, Qi |
| author_facet | Jiang, Dihong Cao, Ruoqi Dang, Zhiyuan Huang, Li Zhang, Qingsong Wang, Zhiyu Piao, Shihao Zhu, Shenggao Chang, Jianlong Lin, Zhouchen Tian, Qi |
| contents | While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged this primacy, yet no consensus exists on a universally superior paradigm. Existing benchmarks typically contain fewer than 100 datasets, raising concerns about evaluation sufficiency and potential selection biases. To address these limitations, we introduce OmniTabBench, the largest tabular benchmark to date, comprising 3030 datasets spanning diverse tasks that are comprehensively collected from diverse sources and categorized by industry using large language models. We conduct an unprecedented large-scale empirical evaluation of state-of-the-art models from all model families on OmniTabBench, confirming the absence of a dominant winner. Furthermore, through a decoupled metafeature analysis, which examines individual properties such as dataset size, feature types, feature and target skewness/kurtosis, we elucidate conditions favoring specific model categories, providing clearer, more actionable guidance than prior compound-metric studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06814 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale Jiang, Dihong Cao, Ruoqi Dang, Zhiyuan Huang, Li Zhang, Qingsong Wang, Zhiyu Piao, Shihao Zhu, Shenggao Chang, Jianlong Lin, Zhouchen Tian, Qi Machine Learning Artificial Intelligence While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged this primacy, yet no consensus exists on a universally superior paradigm. Existing benchmarks typically contain fewer than 100 datasets, raising concerns about evaluation sufficiency and potential selection biases. To address these limitations, we introduce OmniTabBench, the largest tabular benchmark to date, comprising 3030 datasets spanning diverse tasks that are comprehensively collected from diverse sources and categorized by industry using large language models. We conduct an unprecedented large-scale empirical evaluation of state-of-the-art models from all model families on OmniTabBench, confirming the absence of a dominant winner. Furthermore, through a decoupled metafeature analysis, which examines individual properties such as dataset size, feature types, feature and target skewness/kurtosis, we elucidate conditions favoring specific model categories, providing clearer, more actionable guidance than prior compound-metric studies. |
| title | OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.06814 |