sample_gaussian

Integer vector. The indices of the features to condition upon. Note that
<code>min(index_given) &gt;= 1</code> and <code>max(index_given) &lt;= m</code>.

index_given

Positive integer. The total number of features.

Numeric matrix. Contains the features of the observation whose
predictions ought to be explained (test data).

x_test

Sample conditional Gaussian variables

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

sample_gaussian: Sample conditional Gaussian variables

Description

Usage

Arguments

Value