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randomForestExplainer
View on CRAN: Click
here
Download and install randomForestExplainer package within the R console
Install from CRAN:
install.packages("randomForestExplainer")
Install from Github:
library("remotes")
install_github("cran/randomForestExplainer")
Install by package version:
library("remotes")
install_version("randomForestExplainer", "0.10.1")
Attach the package and use:
library("randomForestExplainer")
Maintained by
Yue Jiang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-07-15
Latest Update: 2020-07-11
Description:
A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) , Leo Breiman (2001) ).
How to cite:
Yue Jiang (2017). randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance. R package version 0.10.1, https://cran.r-project.org/web/packages/randomForestExplainer. Accessed 21 Nov. 2024.
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imports, suggests or enhances
Complete documentation for randomForestExplainer
Functions, R codes and Examples using
the randomForestExplainer R package
Some associated functions: explain_forest . important_variables . measure_importance . min_depth_distribution . min_depth_interactions . plot_importance_ggpairs . plot_importance_rankings . plot_min_depth_distribution . plot_min_depth_interactions . plot_multi_way_importance . plot_predict_interaction .
Some associated R codes: explain_forest.R . measure_importance.R . min_depth_distribution.R . min_depth_interactions.R . Full randomForestExplainer package functions and examples
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