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| Authors: | Rodney Sparapani, Charles Spanbauer, Robert McCulloch | ||||
| Title: | Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package | ||||
| Abstract: | In this article, we introduce the BART R package which is an acronym for Bayesian additive regression trees. BART is a Bayesian nonparametric, machine learning, ensemble predictive modeling method for continuous, binary, categorical and time-to-event outcomes. Furthermore, BART is a tree-based, black-box method which fits the outcome to an arbitrary random function, f , of the covariates. The BART technique is relatively computationally efficient as compared to its competitors, but large sample sizes can be demanding. Therefore, the BART package includes efficient state-of-the-art implementations for continuous, binary, categorical and time-to-event outcomes that can take advantage of modern off-the-shelf hardware and software multi-threading technology. The BART package is written in C++ for both programmer and execution efficiency. The BART package takes advantage of multi-threading via forking as provided by the parallel package and OpenMP when available and supported by the platform. The ensemble of binary trees produced by a BART fit can be stored and re-used later via the R predict function. In addition to being an R package, the installed BART routines can be called directly from C++. The BART package provides the tools for your BART toolbox. | ||||
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Page views:: 733. Submitted: 2019-01-23. Published: 2021-01-14. |
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| Paper: |
Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package
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| DOI: |
10.18637/jss.v097.i01
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This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |