c(person(given = "Szymon",
family = "Maksymiuk",
role = c("aut", "cre"),
email = "sz.maksymiuk@gmail.com"),
person(given = "Przemyslaw",
family = "Biecek",
email = "przemyslaw.biecek@gmail.com",
role = c("aut"),
comment = c(ORCID = "0000-0001-8423-1823")))
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.
Szymon Maksymiuk [aut, cre],
Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>)

auditor, gbm, ggrepel, h2o, mljar, mlr, randomForest,
rmarkdown, testthat

c(person(given = "Szymon",
family = "Maksymiuk",
role = c("aut", "cre"),
email = "sz.maksymiuk@gmail.com"),
person(given = "Przemyslaw",
family = "Biecek",
email = "przemyslaw.biecek@gmail.com",
role = c("aut"),
comment = c(ORCID = "0000-0001-8423-1823")),
person("Katarzyna", "Pekala", role = "aut"),
person("Anna", "Kozak", role = "ctb"))
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, 'java' 'h2o' library and
'mljar' API. 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.
reticulate, ggplot2, glmnet, ggdendro, gridExtra
auditor, ingredients, gbm, ggrepel, h2o, mljar, mlr, mlr3,
randomForest, rmarkdown, rpart, xgboost, testthat
Szymon Maksymiuk [aut, cre],
Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>),
Katarzyna Pekala [aut],
Anna Kozak [ctb]