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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 |
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 |
Reference manual: | triplot.pdf |
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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These binaries (installable software) and packages are in development.
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