Accurate predictions on small data with a tabular foundation model.

ABSTRACT: Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science^1,2. 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^3-5, gradient-boosted decision trees^6-9 have dominated tabular data for the past 20 years. 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 sampl

SUBMITTER: Hollmann N 

PROVIDER: S-EPMC11711098 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature