Explaining and Visualizing Random Forests in Terms of Variable Importance [R package randomForestExplainer version 0.10.1]

randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable Importance

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) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).

Version: 0.10.1

Depends: R (≥ 3.0)

Imports: data.table (≥ 1.10.4), dplyr (≥ 0.7.1), DT (≥ 0.2), GGally (≥ 1.3.0), ggplot2 (≥ 2.2.1), ggrepel (≥ 0.6.5), randomForest (≥ 4.6.12), ranger (≥ 0.9.0), reshape2 (≥ 1.4.2), rmarkdown (≥ 1.5)

Suggests: knitr, MASS (≥ 7.3.47), testthat

Published: 2020年07月11日

DOI: 10.32614/CRAN.package.randomForestExplainer

Author: Aleksandra Paluszynska [aut], Przemyslaw Biecek [aut, ths], Yue Jiang ORCID iD [aut, cre]

Maintainer: Yue Jiang <rivehill at gmail.com>

License: GPL-2 | GPL-3 [expanded from: GPL]

URL: https://github.com/ModelOriented/randomForestExplainer

NeedsCompilation: no

Materials: README, NEWS

CRAN checks: randomForestExplainer results