VSURF: Variable Selection Using Random Forests
Version 1.0.3

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.

Getting started

Package details

AuthorRobin Genuer [aut, cre], Jean-Michel Poggi [aut], Christine Tuleau-Malot [aut]
Date of publication2016-04-26 16:50:28
MaintainerRobin Genuer <[email protected]>
LicenseGPL (>= 2)
Version1.0.3
URL https://github.com/robingenuer/VSURF
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("VSURF")

Try the VSURF package in your browser

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

VSURF documentation built on May 29, 2017, 8:35 p.m.