文章基本信息

标题：ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
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作者：Marvin N. Wright ; Andreas Ziegler
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2017
卷号：77
期号：1
页码：1-17
语种：English
出版社：University of California, Los Angeles
摘要：We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and compare runtime and memory usage with other implementations. The new software proves to scale best with the number of features, samples, trees, and features tried for splitting. Finally, we show that ranger is the fastest and most memory efficient implementation of random forests to analyze data on the scale of a genome-wide association study.
关键词：C++;classification;machine learning;R;random forests;Rcpp;recursive partitioning;survival analysis