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. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  2. Limbeck, Jan; Bisdom, Kevin; Lanz, Fabian; Park, Timothy; Barbaro, Eduardo; Bourne, Stephen; Kiraly, Franz; Bierman, Stijn; Harris, Chris; Nevenzeel, Keimpe; den Bezemer, Taco; van Elk, Jan: Using machine learning for model benchmarking and forecasting of depletion-induced seismicity in the Groningen gas field (2021)
  3. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  4. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  5. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  6. Cerqueira, Vitor; Torgo, Luis; Mozetič, Igor: Evaluating time series forecasting models: an empirical study on performance estimation methods (2020)
  7. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  8. Hornung, Roman: Ordinal forests (2020)
  9. Mišić, Velibor V.: Optimization of tree ensembles (2020)
  10. Plečko, Drago; Meinshausen, Nicolai: Fair data adaptation with quantile preservation (2020)
  11. Ribeiro, Rita P.; Moniz, Nuno: Imbalanced regression and extreme value prediction (2020)
  12. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  13. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  14. Schmid, Matthias; Welchowski, Thomas; Wright, Marvin N.; Berger, Moritz: Discrete-time survival forests with Hellinger distance decision trees (2020)
  15. Tomita, Tyler M.; Browne, James; Shen, Cencheng; Chung, Jaewon; Patsolic, Jesse L.; Falk, Benjamin; Priebe, Carey E.; Yim, Jason; Burns, Randal; Maggioni, Mauro; Vogelstein, Joshua T.: Sparse projection oblique randomer forests (2020)
  16. van den Bergh, Don; Bogaerts, Stefan; Spreen, Marinus; Flohr, Rob; Vandekerckhove, Joachim; Batchelder, William H.; Wagenmakers, Eric-Jan: Cultural consensus theory for the evaluation of patients’ mental health scores in forensic psychiatric hospitals (2020)
  17. Athey, Susan; Tibshirani, Julie; Wager, Stefan: Generalized random forests (2019)
  18. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  19. Franzin, Alberto; Stützle, Thomas: Revisiting simulated annealing: a component-based analysis (2019)
  20. Lyubchich, Vyacheslav; Woodland, Ryan J.: Using isotope composition and other node attributes to predict edges in fish trophic networks (2019)

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