yhat.WrappedModel

yhat.H2ORegressionModel

yhat.H2OBinomialModel

yhat.H2OMultinomialModel

yhat.scikitlearn_model

yhat.keras

yhat.LearnerRegr

yhat.LearnerClassif

yhat.GraphLearner

yhat.xgb.Booster

yhat.workflow

yhat.model_stack

These functions are default predict functions.
Each function returns a single numeric score for each new observation.
Those functions are very important since information from many models have to be extracted with various techniques.

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.

Szymon Maksymiuk

DALEXtra

Extension for 'DALEX' Package

Przemyslaw Biecek

Hubert Baniecki

Anna Kozak

yhat.WrappedModel function

<dl><dt>X.model</dt>
<dd>object - a model to be explained</dd>
<dt>newdata</dt>
<dd>data.frame or matrix - observations for prediction</dd>
<dt>...</dt>
<dd>other parameters that will be passed to the predict function</dd></dl>

Arguments

Wrapper over the predict function — yhat.WrappedModel

<dl>

<dt>X.model</dt>
<dd>object - a model to be explained</dd>


<dt>newdata</dt>
<dd>data.frame or matrix - observations for prediction</dd>


<dt>...</dt>
<dd>other parameters that will be passed to the predict function</dd>

</dl>

Wrapper over the predict function

yhat.WrappedModel: Wrapper over the predict function

Description

Usage

Value

Arguments

Details