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 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 

hat_matrix_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></dl>

Arguments

Author

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

</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