Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications).
|Author||Ramon Diaz-Uriarte <email@example.com>|
|Date of publication||2017-07-10 13:54:22 UTC|
|Maintainer||Ramon Diaz-Uriarte <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|URL||http://ligarto.org/rdiaz/Software/Software.html http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html https://github.com/rdiaz02/varSelRF|
|Package repository||View on CRAN|
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