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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 |
Reference manual: | DALEXtra.pdf |
Package source: | DALEXtra_2.3.0.tar.gz |
Windows binaries: | r-devel: DALEXtra_2.3.0.zip, r-release: DALEXtra_2.3.0.zip, r-oldrel: DALEXtra_2.3.0.zip |
macOS binaries: | r-release (arm64): DALEXtra_2.3.0.tgz, r-oldrel (arm64): DALEXtra_2.3.0.tgz, r-release (x86_64): DALEXtra_2.3.0.tgz, r-oldrel (x86_64): DALEXtra_2.3.0.tgz |
Old sources: | DALEXtra archive |
Reverse imports: | tidysdm, viralx |
Reverse suggests: | marginaleffects, mlr3shiny |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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