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

By Marvin N. Wright, Andreas Ziegler. 2015

3,542 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology, Statistics and Probability

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Citations in the corpus

Forest Floor Visualizations of Random Forests (2016, background): “…[3] has achieved the most journal citations, other later decision tree ensemble models/algorithms such as ExtraTrees [14], conditional inference forest [8], Aborist [21], Ranger [26] and sklearn.random.forest [17] will often outperform the original RF on either prediction performance and/or speed.”
Generalized random forests (2016, methodology): “In line with this approach, our generalized random forest software package builds on the carefully optimized ranger implementation of regression forest splitting rules (Wright and Ziegler, 2017).”
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, methodology): “forests, as implemented in the ranger package in R [36].”
Some methods for heterogeneous treatment effect estimation in high dimensions (2017, methodology): “We are able to do so quickly using the fast implementation in the R package ranger (Wright and Ziegler, 2015).”
Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library (2019, methodology): “Random forest was implemented in R using the ranger package [64], where the number of trees to build is provided using the num.”
Interpretable Random Forests via Rule Extraction (2020, methodology): “Our R / C++ package sirus is adapted from ranger , a fast random forests implementation (Wright & Ziegler, 2017).”
Small Sample Size Solutions (2020, methodology): “Internally, MetaForest relies on the ranger R package; a fast implementation of the random forests in C++ (Wright & Ziegler, 2015).”
Evaluating time series forecasting models: an empirical study on performance estimation methods (2019, methodology): “Notwithstanding, other learning algorithms were tested, namely the lasso [39] and a random forest [41].”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “Variable importance metrics (VIMs) are not always reliable, as they are affected by high correlations and interactions (see e.g., Boulesteix et al. [16], Ziegler and König [17]).”