sample_gaussian

Sample conditional Gaussian variables

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 

sample_gaussian function

<dl><dt>index_given</dt>
<dd>Integer vector. The indices of the features to condition upon. Note that
<code>min(index_given) &gt;= 1</code> and <code>max(index_given) &lt;= m</code>.</dd>
<dt>m</dt>
<dd>Positive integer. The total number of features.</dd>
<dt>x_test</dt>
<dd>Numeric matrix. Contains the features of the observation whose
predictions ought to be explained (test data).</dd></dl>

Arguments

Author

Sample conditional Gaussian variables — sample_gaussian

<dl>

<dt>index_given</dt>
<dd>Integer vector. The indices of the features to condition upon. Note that
<code>min(index_given) &gt;= 1</code> and <code>max(index_given) &lt;= m</code>.</dd>


<dt>m</dt>
<dd>Positive integer. The total number of features.</dd>


<dt>x_test</dt>
<dd>Numeric matrix. Contains the features of the observation whose
predictions ought to be explained (test data).</dd>

</dl>

Sample conditional Gaussian variables

sample_gaussian: Sample conditional Gaussian variables

Description

Usage

Value

Arguments

Author