default_doc_export

Exported documentation helper function.

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 

default_doc_export function

<dl><dt>internal</dt>
<dd>List.
Not used directly, but passed through from <code>explain()</code>.</dd>
<dt>iter</dt>
<dd>Integer.
The iteration number. Only used internally.</dd>
<dt>index_features</dt>
<dd>Positive integer vector. Specifies the id_coalition to
apply to the present method. <code>NULL</code> means all coalitions. Only used internally.</dd></dl>

Arguments

Exported documentation helper function. — default_doc_export

<dl>

<dt>internal</dt>
<dd>List.
Not used directly, but passed through from <code>explain()</code>.</dd>


<dt>iter</dt>
<dd>Integer.
The iteration number. Only used internally.</dd>


<dt>index_features</dt>
<dd>Positive integer vector. Specifies the id_coalition to
apply to the present method. <code>NULL</code> means all coalitions. Only used internally.</dd>

</dl>

Exported documentation helper function.

default_doc_export: Exported documentation helper function.

Description

Usage

Arguments