rerf: Randomer Forest

R-RerF (aka Randomer Forest (RerF) or Random Projection Forests) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2> which is similar to Random Forest - Random Combination (Forest-RC) developed by Breiman (2001) <doi:10.1023/A:1010933404324>. Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal. To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along. This package, 'rerf', implements RerF which is similar to Forest-RC. The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.

Getting started

Package details

AuthorJesse Patsolic [ctb, cre], Benjamin Falk [ctb], Jaewon Chung [ctb], James Browne [aut], Tyler Tomita [aut], Joshua Vogelstein [ths]
MaintainerJesse Patsolic <[email protected]>
LicenseApache License 2.0 | file LICENSE
Version2.0.2
URL https://github.com/neurodata/R-RerF
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("rerf")

Try the rerf package in your browser

Any scripts or data that you put into this service are public.

rerf documentation built on Dec. 4, 2018, 5:04 p.m.