print.scikitlearn_set

<p>a list from explainer created with <code><a rd-options="" href="/link/explain_scikitlearn?package=DALEXtra&version=0.2.1" data-mini-rdoc="DALEXtra::explain_scikitlearn">explain_scikitlearn</a></code></p>

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.

Szymon Maksymiuk

DALEXtra

Extension for 'DALEX' Package

Przemyslaw Biecek

Katarzyna Pekala

Anna Kozak

print.scikitlearn_set function

<p>a list from explainer created with <code><a rd-options='' href='explain_scikitlearn'>explain_scikitlearn</a></code></p>

print.scikitlearn_set: Prints scikitlearn_set class

Description

Usage

Arguments