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

08/18/2015
by   Marvin N. Wright, et al.
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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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Code Repositories

missRanger

R package "missRanger" for fast imputation of missing values by random forests.


view repo

missRanger

:exclamation: This is a read-only mirror of the CRAN R package repository. missRanger — Fast Imputation of Missing Values


view repo
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