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

arXiv: https://arxiv.org/abs/1508.04409 · HTML: https://mlsanity.com/paper/1508.04409/

## Key facts

- Cited by 172 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2015.

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## Abstract (arXiv)

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.