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ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Open accessAbstract
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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Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Modeling Survival Data: Extending the Cox Model
Therneau, T. M., & Grambsch, P. M. (2000) · Statistics for Biology and Health · doi:10.1007/978-1-4757-3294-8 · Cited by 8,674
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
GenABEL: an R library for genome-wide association analysis
Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm108 · Cited by 1,836 · cited 2×
“Genotypes are stored in a memory efficient binary format, as inspired by Aulchenko et al. (2007); see Section 5 for the effect on memory usage.”
BackgroundMethodKruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300068 · Cited by 97
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Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733
Self-citationA comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670
Soil carbon debt of 12,000 years of human land use
Sanderman, J., Hengl, T., & Fiske, G. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706103114 · Cited by 1,495
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046
Modelling species presence‐only data with random forests
Valavi, R., Elith, J., Lahoz‐Monfort, J. J., & Guillera‐Arroita, G. (2021) · Ecography · doi:10.1111/ecog.05615 · Cited by 307
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113290 · Cited by 304
“…with position dropout rates in specific business units for male candidates. a Note that both the RF and GBM models and their implementations are generally robust to noisy and high dimensionality datasets, since they base their decisions on multiple permutations of the dataset (see [56,66–69]).”
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Lessler, J., Grabowski, M. K., Grantz, K. H., Badillo-Goicoechea, E., Metcalf, C. J. E., Lupton-Smith, C., Azman, A. S., & Stuart, E. A. (2021) · Science · doi:10.1126/science.abh2939 · Cited by 233
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Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
Bommert, A., Welchowski, T., Schmid, M., & Rahnenführer, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab354 · Cited by 182
Human Skin, Oral, and Gut Microbiomes Predict Chronological Age
Huang, S., Haiminen, N., Carrieri, A.-P., Hu, R., Jiang, L., Parida, L., Russell, B., Allaband, C., Zarrinpar, A., Vázquez-Baeza, Y., et al. (2020) · mSystems · doi:10.1128/msystems.00630-19 · Cited by 167
Jacobson, N. C., & Chung, Y. J. (2020) · Sensors · doi:10.3390/s20123572 · Cited by 139
“Note that a grid search was used to optimize the number of trees to grow (based on a sequence of length three between the number of predictors) and to select the split tree rule based on variance or extremely randomized trees [61,62].”
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Acker, M., Hogle, S. L., Berube, P. M., Hackl, T., Coe, A., Stepanauskas, R., Chisholm, S. W., & Repeta, D. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113386119 · Cited by 76
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Ojeda, F. M., Jansen, M. L., Thiéry, A., Blankenberg, S., Weimar, C., Schmid, M., & Ziegler, A. (2023) · Statistics in Medicine · doi:10.1002/sim.9921 · Cited by 71
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Pardo, M. A., Fristrup, K., Lolchuragi, D. S., Poole, J. H., Granli, P., Moss, C., Douglas-Hamilton, I., & Wittemyer, G. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02420-w · Cited by 62
A data-driven approach for predicting the impact of drugs on the human microbiome
Algavi, Y. M., & Borenstein, E. (2023) · Nature Communications · doi:10.1038/s41467-023-39264-0 · Cited by 54
Juarez-Martinez, E. L., Sprengers, J. J., Cristian, G., Oranje, B., van Andel, D. M., Avramiea, A.-E., Simpraga, S., Houtman, S. J., Hardstone, R., Gerver, C., et al. (2023) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.08.009 · Cited by 48
Whittaker, C., Hamlet, A., Sherrard-Smith, E., Winskill, P., Cuomo-Dannenburg, G., Walker, P. G. T., Sinka, M., Pironon, S., Kumar, A., Ghani, A., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2216142120 · Cited by 43
Alexander, H., Hu, S. K., Krinos, A. I., Pachiadaki, M., Tully, B. J., Neely, C. J., & Reiter, T. (2021) · doi:10.1101/2021.07.25.453713 · Cited by 67
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Anderson, D., Baynam, G., Blackwell, J. M., & Lassmann, T. (2019) · Nature Communications · doi:10.1038/s41467-019-13345-5 · Cited by 24
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Kim, H. B., Nguyen, H. T., Jin, Q., Tamby, S., Gelaf Romer, T., Sung, E., Liu, R., Greenstein, J. L., Suarez, J. I., Storm, C., et al. (2022) · Anaesthesia Critical Care & Pain Medicine · doi:10.1016/j.accpm.2021.101015 · Cited by 18
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Morlighem, C., Chaiban, C., Georganos, S., Brousse, O., Van de Walle, J., van Lipzig, N. P. M., Wolff, E., Dujardin, S., & Linard, C. (2022) · Remote Sensing · doi:10.3390/rs14215381 · Cited by 6
Hagar, N., & Shaw, A. (2022) · Journal of Communication · doi:10.1093/joc/jqac032 · Cited by 6
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Laimighofer, J., Melcher, M., & Laaha, G. (2021) · doi:10.5194/hess-2021-481 · Cited by 30
“There are several packages in R that can estimate RF models, but due to the computational burden of the study we used the fast ranger package (Wright and Ziegler, 2017).”
MethodInfluential
Metadata
- Authors
- Wright, M. N., & Ziegler, A.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2017
- Volume
- 77
- Issue
- 1
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2157395790
- Cited by
- 3,340
- References
- 5
- FWCI
- 90.46
- Type
- journal article
- Language
- en
- Open access
- diamond
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