Ivan Rubachev, Nikita Lazarev, Artem Babenko · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26794 · open-access PDF
最近在表格式机器学习领域的工作强调了多样化和真实的评估设置的重要性。然而,大规模(1M+)数据的区别仍然未被探索。为了研究培训数据的扩展,我们组合了8个真实世界的表格数据集,包含大约1000万个培训示例,保持任务的自然分布变化,并使用随机分割评估作为控制。
Machine translation (Meta M2M100); the English original follows.
Recent work in tabular machine learning has emphasized the importance of diverse and realistic evaluation settings. The niche of large-scale (1M+) data, however, remains under-explored. To study training-data scaling, we assemble eight real-world tabular datasets with up to around ten million training examples, retaining the natural distribution shifts of the tasks and using random-split evaluation as a control. We find that GBDTs and MLP-based models show broadly similar scaling trends, with gains from both additional training data and algorithmic improvements. Tabular foundation models show
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