TabBench-Bio：面向高维生物医学表格的机器学习活基准

中文摘要 AI 辅助

生物医学表格通常将数千个测量变量与仅数十或数百个标记样本相结合，这种情形在通用表格基准中代表性不足。我们引入了TabBench-Bio，一个包含43个跨越多个领域的生物医学数据集的活性和交互式基准。在共享的交叉验证协议下，我们比较了经典估计器、神经网络和表格基础模型在28个特征-样本操作点上的表现。在10,000个特征和100个训练样本的参考单元中，RealTabPFN v2.5具有最高的点估计值，紧随其后的是Logistic Regression和TabDPT，后两者的点估计值几乎相同。对目标池进行的配对自助法将RealTabPFN v2.5与Logistic Regression区分开来，差距为145 Elo（95%区间[59, 232]）。表格基础模型通常占据领先排名，而最强的配置取决于操作点和生物医学模态。AutoML框架AutoGluon，使用其一小时的“极端”预设，被配置为单独的资源密集型参考，并在参考单元处报告。折叠级别的预测、运行状态和确定性聚合使每个报告的结果都可复现和重用。我们邀请社区贡献：TabBench-Bio旨在成长，我们欢迎提交新的生物医学表格数据集，特别是来自代表性不足的测定和临床终点的数据集，以纳入未来版本。交互式排行榜可在以下网址获取：此https URL

英文摘要

Biomedical tables often combine thousands of measured variables with only tens or hundreds of labelled samples, a regime that is poorly represented in general-purpose tabular benchmarks. We introduce TabBench-Bio, a living and interactive benchmark of 43 biomedical datasets spanning multiple domains. Under a shared cross-validation protocol, we compare classical estimators, neural networks, and tabular foundation models across 28 feature-by-sample operating points. At the reference cell of 10,000 features and 100 training samples, RealTabPFN 2.5 has the highest point estimate, closely followed by TabPFN 3 and Logistic Regression. A paired bootstrap over the target pool separates RealTabPFN 2.5 from TabPFN 3 by 87 Elo (95\% interval [43, 129]). Tabular foundation models generally occupy the leading ranks, while the strongest configuration depends on the operating point and biomedical modality. The AutoML framework AutoGluon, using its one-hour "extreme" preset, is configured as a separate resource-intensive reference and reported here at the reference and full cell. Fold-level predictions, run status, and deterministic aggregations make every reported result reproducible and reusable. The benchmark is open to contributions of new biomedical datasets. The interactive leaderboard is available at https://tabbench-bio.eu.

发表机构

• University of Tübingen（蒂宾根大学）

机构由 AI 辅助整理，请以论文原文为准。