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rfVarImpOOB  

Unbiased Variable Importance for Random Forests
View on CRAN: Click here


Download and install rfVarImpOOB package within the R console
Install from CRAN:
install.packages("rfVarImpOOB")

Install from Github:
library("remotes")
install_github("cran/rfVarImpOOB")

Install by package version:
library("remotes")
install_version("rfVarImpOOB", "1.0.3")



Attach the package and use:
library("rfVarImpOOB")
Maintained by
Markus Loecher
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-10-18
Latest Update: 2022-07-01
Description:
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 <doi:10.1080/03610926.2020.1764042>. 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.
How to cite:
Markus Loecher (2020). rfVarImpOOB: Unbiased Variable Importance for Random Forests. R package version 1.0.3, https://cran.r-project.org/web/packages/rfVarImpOOB
Previous versions and publish date:
1.0.1 (2020-10-18 11:00), 1.0 (2019-04-05 14:50)
Other packages that cited rfVarImpOOB R package
View rfVarImpOOB citation profile
Other R packages that rfVarImpOOB depends, imports, suggests or enhances
Functions, R codes and Examples using the rfVarImpOOB R package
Some associated functions: Accuracy . GiniImportanceForest . GiniImportanceTree . InOutBags . Mode . arabidopsis . gini_index . gini_process . lpnorm . mlogloss . plotVI . plotVI2 . preorder2 . rfTitanic . splitBag . titanic_train . 
Some associated R codes: GetNodeInfo_SingleTree.R . InOutBags.R . LossFunctions.R . allFuns.R . plotVI.R .  Full rfVarImpOOB package functions and examples
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