triplot: Explaining Correlated Features in Machine Learning Models (original) (raw)
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 |
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