{"title":"非参数机器学习和贝叶斯加性回归树的高效计算:BART R包","authors":"R. Sparapani, Charles Spanbauer, R. McCulloch","doi":"10.18637/JSS.V097.I01","DOIUrl":null,"url":null,"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.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"115 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"72","resultStr":"{\"title\":\"Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package\",\"authors\":\"R. Sparapani, Charles Spanbauer, R. McCulloch\",\"doi\":\"10.18637/JSS.V097.I01\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":17237,\"journal\":{\"name\":\"Journal of Statistical Software\",\"volume\":\"115 1\",\"pages\":\"\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2021-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"72\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.18637/JSS.V097.I01\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18637/JSS.V097.I01","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package
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.
期刊介绍:
The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.