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