sample_copula

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 observation whose predictions ought to be explained (test data),
after quantile-transforming them to standard Gaussian variables.

x_test_gaussian

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

x_test

Sample conditional variables using the Gaussian copula approach

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.

sample_copula: Sample conditional variables using the Gaussian copula approach

Description

Usage

Arguments

Value