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The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories<jats:sup>3\u20135<\/jats:sup>, gradient-boosted decision trees<jats:sup>6\u20139<\/jats:sup> have dominated tabular data for the past 20\u2009years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8\u2009s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4\u2009h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a\u00a0learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains.<\/jats:p>","DOI":"10.1038\/s41586-024-08328-6","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T16:05:17Z","timestamp":1736352317000},"page":"319-326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":534,"title":["Accurate predictions on small data with a tabular foundation model"],"prefix":"10.1038","volume":"637","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8556-518X","authenticated-orcid":false,"given":"Noah","family":"Hollmann","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0795-6097","authenticated-orcid":false,"given":"Samuel","family":"M\u00fcller","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1181-0549","authenticated-orcid":false,"given":"Lennart","family":"Purucker","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0688-9435","authenticated-orcid":false,"given":"Arjun","family":"Krishnakumar","sequence":"additional","affiliation":[]},{"given":"Max","family":"K\u00f6rfer","sequence":"additional","affiliation":[]},{"given":"Shi Bin","family":"Hoo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5518-7445","authenticated-orcid":false,"given":"Robin Tibor","family":"Schirrmeister","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-3694","authenticated-orcid":false,"given":"Frank","family":"Hutter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,8]]},"reference":[{"key":"8328_CR1","doi-asserted-by":"publisher","first-page":"7499","DOI":"10.1109\/TNNLS.2022.3229161","volume":"35","author":"V Borisov","year":"2024","unstructured":"Borisov, V. et al. 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Bosch are related to this work: DE202021105192U1 and DE102021210775A1. The authors do not have any ownership rights to these patent applications. F.H. and N.H. are affiliated with PriorLabs, a company focused on developing tabular foundation models. The authors declare no other competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}