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) , Strobl et al (2007) and Breiman (2001) . 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.


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install.packages("rfVarImpOOB")

1.0 by Markus Loecher, 6 months ago


Browse source code at https://github.com/cran/rfVarImpOOB


Authors: Markus Loecher <[email protected]>


Documentation:   PDF Manual  


GPL (>= 2) license


Imports ggplot2, binaryLogic, dplyr, titanic, prob, ggpubr, magrittr

Depends on stats, randomForest

Suggests knitr, rmarkdown


See at CRAN