triplot: Explaining Correlated Features in Machine Learning Models

Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <doi:10.48550/arXiv.1806.08915>.

Version:	1.3.0
Depends:	R (≥ 3.6)
Imports:	ggplot2, DALEX (≥ 1.3), glmnet, ggdendro, patchwork
Suggests:	testthat, knitr, randomForest, mlbench, ranger, gbm, covr
Published:	2020-07-13
DOI:	10.32614/CRAN.package.triplot
Author:	Katarzyna Pekala [aut, cre], Przemyslaw Biecek [aut]
Maintainer:	Katarzyna Pekala <katarzyna.pekala at gmail.com>
BugReports:	https://github.com/ModelOriented/triplot/issues
License:	GPL-3
URL:	https://github.com/ModelOriented/triplot
NeedsCompilation:	no
Language:	en-US
Materials:	NEWS
CRAN checks:	triplot results

Documentation:

Reference manual:

triplot.pdf

Downloads:

Package source:	triplot_1.3.0.tar.gz
Windows binaries:	r-devel: triplot_1.3.0.zip, r-release: triplot_1.3.0.zip, r-oldrel: triplot_1.3.0.zip
macOS binaries:	r-release (arm64): triplot_1.3.0.tgz, r-oldrel (arm64): triplot_1.3.0.tgz, r-release (x86_64): triplot_1.3.0.tgz, r-oldrel (x86_64): triplot_1.3.0.tgz
Old sources:	triplot archive

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