ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
From MaRDI portal
Publication:101396
DOI10.48550/ARXIV.1508.04409arXiv1508.04409MaRDI QIDQ101396
Author name not available (Why is that?)
Publication date: 18 August 2015
Abstract: 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.
This page was built for publication: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q101396)
