model_checker

model_checker.default

model_checker.ar

model_checker.Arima

model_checker.forecast_ARIMA

model_checker.glm

model_checker.lm

model_checker.gam

model_checker.ranger

model_checker.workflow

model_checker.xgb.Booster

The function checks whether the model given by <code>x</code> is supported.
If <code>x</code> is not a supported model the function will return an error message, otherwise it return NULL
(meaning all types of models with this class is supported)

internal

Complex machine learning models are often hard to interpret. However, in
many situations it is crucial to understand and explain why a model made a specific
prediction. Shapley values is the only method for such prediction explanation framework
with a solid theoretical foundation. Previously known methods for estimating the Shapley
values do, however, assume feature independence. This package implements methods which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.
An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Lars Henry Berge Olsen

Annabelle Redelmeier

Jon Lachmann

Nikolai Sellereite

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

model_checker function

<dl><dt>x</dt>
<dd>Model object for the model to be explained.</dd></dl>

Arguments

Check that the type of model is supported by the native implementation of the model class — model_checker

<dl>

<dt>x</dt>
<dd>Model object for the model to be explained.</dd>

</dl>

Check that the type of model is supported by the native implementation of the model class

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model_checker: Check that the type of model is supported by the native implementation of the model class

Description

Usage

Value

Arguments

See Also