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.
Package details 


Author  Robin Genuer [aut, cre], JeanMichel Poggi [aut], Christine TuleauMalot [aut] 
Date of publication  20160426 16:50:28 
Maintainer  Robin Genuer <Robin.Genuer@isped.ubordeaux2.fr> 
License  GPL (>= 2) 
Version  1.0.3 
URL  https://github.com/robingenuer/VSURF 
Package repository  View on CRAN 
Installation 
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