ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
This provides a practical tool for researchers handling large-scale genomic data, though it is incremental as it optimizes an existing method.
The authors tackled the challenge of efficiently implementing random forests for high-dimensional data by introducing ranger, a C++/R software that scales best with features, samples, trees, and split features, proving to be the fastest and most memory-efficient implementation for genome-wide association studies.
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.