DALEXtra: Extension for 'DALEX' Package
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.
||R (≥ 3.5.0), DALEX (≥ 1.3)
||auditor, ingredients, gbm, ggrepel, h2o, iml, lime, localModel, mlr, mlr3, randomForest, recipes, rmarkdown, rpart, xgboost, testthat, tidymodels
Anna Kozak [ctb],
Hubert Baniecki [ctb]
||Szymon Maksymiuk <sz.maksymiuk at gmail.com>
||GPL-2 | GPL-3 [expanded from: GPL]
||DALEXtra citation info
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