ranger: A Fast Implementation of Random Forests
Marvin N. Wright
ranger is a fast implementation of random forest (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, probability estimation and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). For probability estimation forests see Malley et al. (2012).
ranger is written in C++, but a version for R is available, too. We recommend to use the R version. It is easy to install and use and the results are readily available for further analysis. The R version is as fast as the standalone C++ version.
To install the ranger R package from CRAN, just run
R version >= 3.1 is required. With recent R versions, multithreading on Windows platforms should just work. If you compile yourself, the new RTools toolchain is required.
To install the development version from GitHub using
Standalone C++ version
To install the C++ version of ranger in Linux or Mac OS X you will need a compiler supporting C++11 (i.e. gcc >= 4.7 or Clang >= 3.0) and Cmake. To build start a terminal from the ranger main directory and run the following commands
cd cpp_version mkdir build cd build cmake .. make
After compilation there should be an executable called "ranger" in the build directory.
To run the C++ version in Microsoft Windows please cross compile or ask for a binary.
For usage of the R version see ?ranger in R. Most importantly, see the Examples section. As a first example you could try
ranger(Species ~ ., data = iris)
Standalone C++ version
In the C++ version type
for a list of commands. First you need a training dataset in a file. This file should contain one header line with variable names and one line with variable values per sample. Variable names must not contain any whitespace, comma or semicolon. Values can be seperated by whitespace, comma or semicolon but can not be mixed in one file. A typical call of ranger would be for example
ranger --verbose --file data.dat --depvarname Species --treetype 1 --ntree 1000 --nthreads 4
If you find any bugs, or if you experience any crashes, please report to us. If you have any questions just ask, we won't bite.
Please cite our paper if you use ranger.
- Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.
- Schmid, M., Wright, M. N. & Ziegler, A. (2016). On the use of Harrell’s C for clinical risk prediction via random survival forests. Expert Systems with Applications 63:450-459. http://dx.doi.org/10.1016/j.eswa.2016.07.018.
- Wright, M. N., Dankowski, T. & Ziegler, A. (2017). Unbiased split variable selection for random survival forests using maximally selected rank statistics. Statistics in Medicine. http://dx.doi.org/10.1002/sim.7212.
- Breiman, L. (2001). Random forests. Machine learning 45:5-32.
- Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The Annals of Applied Statistics 2:841-860.
- Malley, J. D., Kruppa, J., Dasgupta, A., Malley, K. G., & Ziegler, A. (2012). Probability machines: consistent probability estimation using nonparametric learning machines. Methods of Information in Medicine 51:74-81.