ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

Marvin N. Wright and Andreas Ziegler

Journal of Statistical Software, 2017, vol. 077, issue i01

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.

Date: 2017-03-31
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:077:i01

DOI: 10.18637/jss.v077.i01

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