aicc_full_cpp

AICc formula for several sets, alternative definition

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_cpp function

<dl><dt>h</dt>
<dd>Numeric. Specifies the scaling (sigma)</dd>
<dt>X_list</dt>
<dd>List</dd>
<dt>mcov_list</dt>
<dd>List</dd>
<dt>S_scale_dist</dt>
<dd>Logical. Indicates whether Mahalanobis distance should be scaled with the
number of variables</dd>
<dt>y_list</dt>
<dd>List.</dd>
<dt>negative</dt>
<dd>Logical.</dd></dl>

Arguments

Author

AICc formula for several sets, alternative definition — aicc_full_cpp

<dl>

<dt>h</dt>
<dd>Numeric. Specifies the scaling (sigma)</dd>


<dt>X_list</dt>
<dd>List</dd>


<dt>mcov_list</dt>
<dd>List</dd>


<dt>S_scale_dist</dt>
<dd>Logical. Indicates whether Mahalanobis distance should be scaled with the
number of variables</dd>


<dt>y_list</dt>
<dd>List.</dd>


<dt>negative</dt>
<dd>Logical.</dd>

</dl>

AICc formula for several sets, alternative definition

aicc_full_cpp: AICc formula for several sets, alternative definition

Description

Usage

Value

Arguments

Author