robingenuer/VSURF: Variable Selection Using Random Forests
Version 1.0.4.9000

Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) .

Getting started

Package details

Maintainer
LicenseGPL (>= 2)
Version1.0.4.9000
URL https://github.com/robingenuer/VSURF
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("robingenuer/VSURF")
robingenuer/VSURF documentation built on Oct. 16, 2018, 11:09 a.m.