Provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in 'R'. 'DALEXtra' creates 'DALEX' Biecek (2018) <doi:10.48550/arXiv.1806.08915> explainer for many type of models including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot.
Version: 2.3.0 Depends: R (≥ 3.5.0), DALEX (≥ 2.4.0) Imports: ggplot2 Suggests: auditor, gbm, ggrepel, h2o, iml, ingredients, lime, localModel, mlr, mlr3, ranger, recipes, reticulate, rmarkdown, rpart, stacks, xgboost, testthat, tidymodels Published: 2023-05-26 DOI: 10.32614/CRAN.package.DALEXtra Author: Szymon Maksymiuk [aut, cre], Przemyslaw Biecek [aut], Hubert Baniecki [aut], Anna Kozak [ctb] Maintainer: Szymon Maksymiuk <sz.maksymiuk at gmail.com> BugReports: https://github.com/ModelOriented/DALEXtra/issues License: GPL-2 | GPL-3 [expanded from: GPL] URL: https://ModelOriented.github.io/DALEXtra/, https://github.com/ModelOriented/DALEXtra NeedsCompilation: no Citation: DALEXtra citation info Materials: NEWS CRAN checks: DALEXtra results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=DALEXtra to link to this page.
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