aicc_full_single_cpp

Temp-function for computing the full AICc with several X's etc

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 

aicc_full_single_cpp function

<dl><dt>X</dt>
<dd>matrix with "covariates"</dd>
<dt>mcov</dt>
<dd>covariance matrix</dd>
<dt>S_scale_dist</dt>
<dd>logical indicating whether the Mahalanobis distance should be scaled with the number of variables</dd>
<dt>h</dt>
<dd>numeric specifying the scaling (sigma)</dd>
<dt>y</dt>
<dd>vector with the "response variable"</dd></dl>

Arguments

Author

Temp-function for computing the full AICc with several X's etc — aicc_full_single_cpp

<dl>

<dt>X</dt>
<dd>matrix with "covariates"</dd>


<dt>mcov</dt>
<dd>covariance matrix</dd>


<dt>S_scale_dist</dt>
<dd>logical indicating whether the Mahalanobis distance should be scaled with the number of variables</dd>


<dt>h</dt>
<dd>numeric specifying the scaling (sigma)</dd>


<dt>y</dt>
<dd>vector with the "response variable"</dd>

</dl>

Temp-function for computing the full AICc with several X's etc

aicc_full_single_cpp: Temp-function for computing the full AICc with several X's etc

Description

Usage

Value

Arguments

Author