[aut]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: | 1.3.0 |
| Depends: | R (≥ 3.6) |
| Imports: | ggplot2, DALEX (≥ 1.3), glmnet, ggdendro, patchwork |
| Suggests: | testthat, knitr, randomForest, mlbench, ranger, gbm, covr |
| Published: | 2020-07-13 |
| 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 |
| 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: triplot_1.3.0.tgz, r-oldrel: triplot_1.3.0.tgz |
| Old sources: | triplot archive |
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