weight_matrix

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

normalize_W_weights

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 L<c3><b8>land (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

Anders L<c3><b8>land

Jens Christian Wahl

Camilla Lingj<c3><a6>rde

Norsk Regnesentral 

weight_matrix function

Calculate weighted matrix — weight_matrix

Calculate weighted matrix

weight_matrix: Calculate weighted matrix

Description

Usage

Arguments

Value