rss_cpp

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 

rss_cpp function

<dl><dt>H</dt>
<dd>Matrix. Output from <code>hat_matrix_cpp</code></dd>
<dt>y</dt>
<dd>Vector, i.e. representing the response variable</dd></dl>

Arguments

Author

sigma_hat_sq-function — rss_cpp

<dl>

<dt>H</dt>
<dd>Matrix. Output from <code>hat_matrix_cpp</code></dd>


<dt>y</dt>
<dd>Vector, i.e. representing the response variable</dd>

</dl>

sigma_hat_sq-function

rss_cpp: sigma_hat_sq-function

Description

Usage

Value

Arguments

Author