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 ORCID iD [aut]
Maintainer: Katarzyna Pekala <katarzyna.pekala at>
License: GPL-3
NeedsCompilation: no
Language: en-US
Materials: NEWS
CRAN checks: triplot results


Reference manual: triplot.pdf


Package source: triplot_1.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


Please use the canonical form to link to this page.