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NOBEL PRIZE 2026

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NIHNATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESR35

Super Greedy Trees

Completed
Institution
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE, FL
Principal investigator
Hemant Ishwaran
Funding
$422K
Duration
2021–2026
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Abstract

Project Summary/Abstract We identify critical weaknesses with Classification and Regression Trees (CART), a widely used base learner for machine learning of big omic-data analysis, and propose to replace these with a fundamentally different type of base learner we call super greedy trees (SGT's). SGT's cut the space in a fundamentally different manner, resulting in a richer partition structure with provable consistency and superior empirical performance. The project will develop a unified SGT framework for big data analysis using machine learning including the treatment of time varying covariate survival analysis, unsupervised learning, highly imbalanced data and multivariate regression. The SGT framework will be deployed within scalable and extensible open source software that will allow NIGMS researchers to deploy them to deal with their challenging big data problems.

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R35NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESSpecial Emphasis Panel[ZRG1-CB-E(55)R]fundamentallyregressionsupertreesbig datalearnerfundamentally differentbig

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