You know what is strange ? the URL for the C++ or the CRAN repository of this software is not listed once in this preprint while all other competing URLs are.
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R by Marvin N. Wright, Andreas Ziegler
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R by Marvin N. Wright, Andreas Ziegler
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.
The C++ implementation is here: http://www.imbs-luebeck.de/imbs/de/node/313 , the R implementation is on CRAN at: http://CRAN.R-project.org/package=ranger
- Ishwaran H, Kogalur U (2015). randomForestSRC: Random Forests for Survival, Regression and Classifi cation. R package version 1.6.1, URL http://CRAN.R-project.org/package=randomForestSRC.
- Seligman M (2015). Rborist: Extensible, Parallelizable Implementation of the Random Forest Algorithm. R package version 0.1-0, URL http://CRAN.R-project.org/package=Rborist
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.
No comments:
Post a Comment