create_marginal_data_gaussian

Given a multivariate Gaussian distribution, this function creates data from specified marginals of said distribution.

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 

create_marginal_data_gaussian function

<dl><dt>n_MC_samples</dt>
<dd>Integer. The number of samples to generate.</dd>
<dt>Sbar_features</dt>
<dd>Vector of integers indicating which marginals to sample from.</dd>
<dt>mu</dt>
<dd>Numeric vector containing the expected values for all features in the multivariate Gaussian distribution.</dd>
<dt>cov_mat</dt>
<dd>Numeric matrix containing the covariance between all features
in the multivariate Gaussian distribution.</dd></dl>

Arguments

Author

Generate marginal Gaussian data using Cholesky decomposition — create_marginal_data_gaussian

<dl>

<dt>n_MC_samples</dt>
<dd>Integer. The number of samples to generate.</dd>


<dt>Sbar_features</dt>
<dd>Vector of integers indicating which marginals to sample from.</dd>


<dt>mu</dt>
<dd>Numeric vector containing the expected values for all features in the multivariate Gaussian distribution.</dd>


<dt>cov_mat</dt>
<dd>Numeric matrix containing the covariance between all features
in the multivariate Gaussian distribution.</dd>

</dl>

Generate marginal Gaussian data using Cholesky decomposition

create_marginal_data_gaussian: Generate marginal Gaussian data using Cholesky decomposition

Description

Usage

Arguments

Author