hat_matrix_cpp

Computing single H matrix in AICc-function using the Mahalanobis distance

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 

hat_matrix_cpp function

<dl><dt>X</dt>
<dd>matrix.</dd>
<dt>mcov</dt>
<dd>matrix
The covariance matrix of X.</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></dl>

Arguments

Author

Computing single H matrix in AICc-function using the Mahalanobis distance — hat_matrix_cpp

<dl>

<dt>X</dt>
<dd>matrix.</dd>


<dt>mcov</dt>
<dd>matrix
The covariance matrix of X.</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>

</dl>

Computing single H matrix in AICc-function using the Mahalanobis distance

hat_matrix_cpp: Computing single H matrix in AICc-function using the Mahalanobis distance

Description

Usage

Value

Arguments

Author