inv_gaussian_transform_cpp

Transforms new data to a standardized normal 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 

inv_gaussian_transform_cpp function

<dl><dt>z</dt>
<dd>arma::mat.
The data are the Gaussian Monte Carlos samples to transform.</dd>
<dt>x</dt>
<dd>arma::mat.
The data with the original transformation. Used to conduct the transformation of <code>z</code>.</dd></dl>

Arguments

Author

Transforms new data to a standardized normal distribution — inv_gaussian_transform_cpp

<dl>

<dt>z</dt>
<dd>arma::mat.
The data are the Gaussian Monte Carlos samples to transform.</dd>


<dt>x</dt>
<dd>arma::mat.
The data with the original transformation. Used to conduct the transformation of <code>z</code>.</dd>

</dl>

Transforms new data to a standardized normal distribution

inv_gaussian_transform_cpp: Transforms new data to a standardized normal distribution

Description

Usage

Value

Arguments

Author