bartMachine: Machine Learning with Bayesian Additive Regression Trees

By Adam Kapelner, Justin Bleich. 2013

369 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lihua Lei
2. E.J. Candes
3. Antonio R. Linero
4. Yun Yang
5. Adam Kapelner
6. E. Pitkin
7. A. Goldstein
8. Justin Bleich

In Statistics and Probability

1. Lihua Lei
2. E.J. Candes
3. Antonio R. Linero
4. Yun Yang
5. E. Pitkin
6. A. Goldstein
7. M. Febrero–Bande
8. Adam Kapelner

Citations in the corpus

• Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “We first modeled the response as a function of the 37 covariates as well as treatment to obtain the best fit of the functional relationship using the black-box algorithm BART (implemented by Kapelner and Bleich, 2014) and obtained an in-sample R 2 ≈ 0 .”
• Bayesian regression tree ensembles that adapt to smoothness and sparsity (2017, methodology): “Given enough optimization, we hope that our implementation could reach speeds within a modest factor of existing highly optimized implementations of the BART algorithm (Kapelner and Bleich, 2016).”
• An extensive experimental survey of regression methods (2019, methodology): “bMachine is the Bayesian additive regression tree (bartMachine pack873 age), which consists of a sum of regression trees and a regularization 874 process developed on the parameters of the tree set [26].”
• Conformal inference of counterfactuals and individual treatment effects (2020, methodology): “We use the functions calc credible intervals and calc prediction intervals from the bartMachine package [Kapelner and Bleich, 2016] to compute both intervals.”