Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R | Wright | Journal of Statistical Software
Authors: Marvin N. Wright, Andreas Ziegler
Title: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Abstract: 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.

Page views:: 2528. Submitted: 2014-08-29. Published: 2017-03-31.
Paper: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R     Download PDF (Downloads: 1312)
Supplements:
ranger_0.7.0.tar.gz: R source package Download (Downloads: 66; 107KB)
ranger_cpp_0.5.0.zip: C++ source package Download (Downloads: 54; 96KB)
v77i01-replication.zip: Replication materials Download (Downloads: 58; 111KB)

DOI: 10.18637/jss.v077.i01

by
This work is licensed under the licenses
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.