varSelRF: Variable selection using random forests. 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). You can use rpvm instead of Rmpi if you want but I’ve only tested with Rmpi.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Siroky, David S.: Navigating random forests and related advances in algorithmic modeling (2009)
- Tierney, Luke; Rossini, A.J.; Li, Na: Snow: A parallel computing framework for the R system (2009)