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