rfVarImpOOB: Unbiased Variable Importance for Random Forests

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also Strobl et al (2007) <doi:10.1186/1471-2105-8-25>, Strobl et al (2007) <doi:10.1016/j.csda.2006.12.030> and Breiman (2001) <doi:10.1023/A:1010933404324>. The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split. This gain is aggregated for each split variable in a tree and averaged across trees.

Version: 1.0
Depends: R (≥ 3.2.2), stats, randomForest
Imports: ggplot2, binaryLogic, dplyr, titanic, prob, ggpubr, magrittr
Suggests: knitr, rmarkdown
Published: 2019-04-05
Author: Markus Loecher
Maintainer: Markus Loecher <Markus.Loecher at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: rfVarImpOOB results

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Reference manual: rfVarImpOOB.pdf
Vignettes: Vignette Title
Package source: rfVarImpOOB_1.0.tar.gz
Windows binaries: r-devel: rfVarImpOOB_1.0.zip, r-devel-gcc8: rfVarImpOOB_1.0.zip, r-release: rfVarImpOOB_1.0.zip, r-oldrel: rfVarImpOOB_1.0.zip
OS X binaries: r-release: rfVarImpOOB_1.0.tgz, r-oldrel: rfVarImpOOB_1.0.tgz

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