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

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  1. Berk, Richard A.: Statistical learning from a regression perspective (2020)
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  10. Schmid, Matthias; Welchowski, Thomas; Wright, Marvin N.; Berger, Moritz: Discrete-time survival forests with Hellinger distance decision trees (2020)
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  17. Simon Hediger, Loris Michel, Jeffrey Näf: On the Use of Random Forest for Two-Sample Testing (2019) arXiv
  18. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  19. Janitza, Silke; Celik, Ender; Boulesteix, Anne-Laure: A computationally fast variable importance test for random forests for high-dimensional data (2018)
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