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) <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. Third branch of 'DALEXtra' package is aspect importance analysis that provides instance-level explanations for the groups of explanatory variables.
|Author||Szymon Maksymiuk [aut, cre] (<https://orcid.org/0000-0002-3120-1601>), Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>), Anna Kozak [ctb], Hubert Baniecki [ctb]|
|Maintainer||Szymon Maksymiuk <email@example.com>|
|Package repository||View on CRAN|
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