model_info.WrappedModel

model_info.H2ORegressionModel

model_info.H2OBinomialModel

model_info.scikitlearn_model

model_info.keras

model_info.LearnerRegr

model_info.LearnerClassif

model

- another arguments
Currently supported packages are:<ul>
<li><code>mlr</code> models created with <code>mlr</code> package</li>
<li><code>h2o</code> models created with <code>h2o</code> package</li>
<li><code>scikit-learn</code> models created with <code>scikit-learn</code> pyhton library and accesed via <code>reticulate</code></li>
<li><code>keras</code> models created with <code>keras</code> pyhton library and accesed via <code>reticulate</code></li>
<li><code>mlr3</code> models created with <code>mlr3</code> package</li>
</ul>

This generic function let user extract base information about model. The function returns a named list of class <code>model_info</code> that
contain about package of model, version and task type. For wrappers like <code>mlr</code> or <code>caret</code> both, package and wrapper inforamtion
are stored

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.

model_info.WrappedModel: Exract info from model

Description

Usage

Arguments

Value