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New TabBench-Bio benchmark evaluates ML models on biomedical data

By PulseAugur Editorial · [1 sources] ·

A new benchmark called TabBench-Bio has been introduced to evaluate machine learning models on high-dimensional biomedical datasets. The benchmark includes 43 datasets and compares various models, including classical estimators, neural networks, and tabular foundation models. At a specific operating point of 10,000 features and 100 training samples, RealTabPFN v2.5 demonstrated the highest performance, followed closely by Logistic Regression and TabDPT. The benchmark is designed to be interactive and grow with community contributions of new biomedical tabular datasets. AI

IMPACT This benchmark could drive the development of more specialized and effective ML models for biomedical research.

RANK_REASON The item is a research paper introducing a new benchmark for machine learning on biomedical data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TabBench-Bio benchmark evaluates ML models on biomedical data

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The item is a research paper introducing a new benchmark for machine learning on biomedical data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jules Kreuer, Sofiane Ouaari, Julia Hellmig, Julius Braitinger, Nico Pfeifer ·

    TabBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables

    arXiv:2609.07441v1 Announce Type: cross Abstract: 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 int…

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