Thursday, December 26, 2024
12/26/2024
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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