Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Adam Kapelner, Justin Bleich
Title: bartMachine: Machine Learning with Bayesian Additive Regression Trees
Abstract: We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.

Page views:: 5055. Submitted: 2013-12-31. Published: 2016-04-04.
Paper: bartMachine: Machine Learning with Bayesian Additive Regression Trees     Download PDF (Downloads: 2328)
Supplements:
bartMachineJARs_1.0.tar.gz: R source package Download (Downloads: 166; 3MB)
bartMachine_1.2.2.tar.gz: R source package Download (Downloads: 142; 1MB)
v70i04-replication.zip: Replication materials Download (Downloads: 137; 388KB)
DOI: 10.18637/jss.v070.i04

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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.