.png){kind=icon}
.png){kind=icon}
Return
Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package
.png){kind=icon}
OA
AI
DOI：10.18637/jss.v097.i01.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
binary trees
black-box
categorical
competing risks
continuous
ensemble predictive model
forking
multinomial
multi-threading
OpenMP
recurrent events
survival analysis
Journal
IF:
8.1
Papers:
622
Citations:
4.6W