CRAN/E | VSURF

VSURF

Variable Selection Using Random Forests

Installation

About

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) .

github.com/robingenuer/VSURF
Bug report File report

Key Metrics

Version 1.2.0
R ≥ 4.2.0
Published 2022-12-15 584 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks VSURF results

Downloads

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Maintainer

Maintainer

Robin Genuer

Authors

Robin Genuer

aut / cre

Jean-Michel Poggi

aut

Christine Tuleau-Malot

aut

Material

NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

VSURF archive

Depends

R ≥ 4.2.0

Imports

doParallel
foreach
parallel
randomForest
rpart

Suggests

testthat
ranger
Rborist

Reverse Imports

armada
MSclassifR