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

We introduce the C++ application and R package ranger. The software is a fast\nimplementation of random forests for high dimensional data. Ensembles of\nclassification, regression and survival trees are supported. We describe the\nimplementation, provide examples, validate the package with a reference\nimplementation, and compare runtime and memory usage with other\nimplementations. The new software proves to scale best with the number of\nfeatures, samples, trees, and features tried for splitting. Finally, we show\nthat ranger is the fastest and most memory efficient implementation of random\nforests to analyze data on the scale of a genome-wide association study.\n