Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package

Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package

: binary trees, black-box, categorical, competing risks, continuous, ensemble predictive model, forking, multinomial, multi-threading, OpenMP, recurrent events, survival
analysis.

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.

Rodney Sparapani

https://www.jstatsoft.org/article/view/v097i01

Medical College of Wisconsin

Januari 2021

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