This paper describes the R package VSURF. Based on random forests, and for both regression and classification problems, it returns two subsets of variables. The first is a subset of important variables including some redundancy which can be relevant for interpretation, and the second one is a smaller subset corresponding to a model trying to avoid redundancy focusing more closely on the prediction objective. The two-stage strategy is based on a preliminary ranking of the explanatory variables using the random forests permutation-based score of importance and proceeds using a stepwise forward strategy for variable introduction. The two proposals can be obtained automatically using data-driven default values, good enough to provide interesting results, but strategy can also be tuned by the user. The algorithm is illustrated on a simulated example and its applications to real datasets are presented.
VSURF, rpart, randomForest, party, ipred, Boruta, varSelRF, spikeSlabGAM, BioMark, mlbench, mixOmics
MachineLearning, Environmetrics, Survival, ChemPhys, Multivariate, Bayesian, HighPerformanceComputing
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For attribution, please cite this work as
Genuer, et al., "The R Journal: VSURF: An R Package for Variable Selection Using Random Forests", The R Journal, 2015
BibTeX citation
@article{RJ-2015-018,
author = {Genuer, Robin and Poggi, Jean-Michel and Tuleau-Malot, Christine},
title = {The R Journal: VSURF: An R Package for Variable Selection Using Random Forests},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-018},
doi = {10.32614/RJ-2015-018},
volume = {7},
issue = {2},
issn = {2073-4859},
pages = {19-33}
}