weight_matrix

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 

weight_matrix function

<dl><dt>X</dt>
<dd>data.table.
Output from <code>create_coalition_table()</code>.</dd>
<dt>normalize_W_weights</dt>
<dd>Logical. Whether to normalize the coalition weights to sum to 1 for
increased numerical stability before solving the WLS (weighted least squares). Applies to all coalitions
except coalitions <code>1</code> and <code>2^m</code>.</dd></dl>

Arguments

Author

Calculate Weighted Matrix — weight_matrix

<dl>

<dt>X</dt>
<dd>data.table.
Output from <code>create_coalition_table()</code>.</dd>


<dt>normalize_W_weights</dt>
<dd>Logical. Whether to normalize the coalition weights to sum to 1 for
increased numerical stability before solving the WLS (weighted least squares). Applies to all coalitions
except coalitions <code>1</code> and <code>2^m</code>.</dd>

</dl>

Calculate Weighted Matrix

weight_matrix: Calculate Weighted Matrix

Description

Usage

Value

Arguments

Author