Random Forester (RerF) is an algorithm developed by Tomita (2016) which is similar to Random Forest  Random Combination (ForestRC) developed by Breiman (2001) . Random Forests create axisparallel, 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 ForestRC, which uses linear combinations of coordinates rather than individual coordinates, to split along. This package, 'rerf', implements RerF which is similar to ForestRC. The difference between the two algorithms is where the random linear combinations occur: ForestRC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.

Maintainer  
License  GPL2 
Version  1.0 
URL 
https://github.com/neurodata/RRerF

Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("neurodata/RRerF")

neurodata/RRerF documentation built on Feb. 20, 2018, 9:22 p.m.