get_list_approaches

Helper function used in <code>explain.combined</code>

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 the method
described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.

Martin Jullum

shapr

Prediction Explanation with Dependence-Aware Shapley Values

Nikolai Sellereite

Annabelle Redelmeier

Anders Løland

Jens Christian Wahl

Camilla Lingjærde

Norsk Regnesentral 

get_list_approaches function

<dl><dt>n_features</dt>
<dd>Integer vector. Note that
<code>length(n_features) &lt;= 2^m</code>, where <code>m</code> equals the number
of features.</dd>
<dt>approach</dt>
<dd>Character vector of length <code>m</code>. All elements should be
either <code>"empirical"</code>, <code>"gaussian"</code> or <code>"copula"</code>.</dd></dl>

Arguments

Author

Helper function used in explain.combined — get_list_approaches

<dl>

<dt>n_features</dt>
<dd>Integer vector. Note that
<code>length(n_features) &lt;= 2^m</code>, where <code>m</code> equals the number
of features.</dd>


<dt>approach</dt>
<dd>Character vector of length <code>m</code>. All elements should be
either <code>"empirical"</code>, <code>"gaussian"</code> or <code>"copula"</code>.</dd>

</dl>

Helper function used in <code>explain.combined</code>

get_list_approaches: Helper function used in `explain.combined`

Description

Usage

Value

Arguments

Author