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) <arXiv:1806.08915>.

Version: 1.2.0
Depends: R (≥ 3.6)
Imports: ggplot2, DALEX (≥ 1.2), glmnet, ggdendro, patchwork
Suggests: testthat, knitr, randomForest, mlbench, ranger, gbm, covr
Published: 2020-06-09
Author: Katarzyna Pekala [aut, cre], Przemyslaw Biecek ORCID iD [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

Downloads:

Reference manual: triplot.pdf
Package source: triplot_1.2.0.tar.gz
Windows binaries: r-devel: triplot_1.2.0.zip, r-release: triplot_1.2.0.zip, r-oldrel: triplot_1.2.0.zip
macOS binaries: r-release: triplot_1.2.0.tgz, r-oldrel: triplot_1.2.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=triplot to link to this page.