vaeac_check_x_colnames

Function that checks the feature names of data and <code>vaeac</code> model

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 

vaeac_check_x_colnames function

<dl><dt>feature_names_vaeac</dt>
<dd>Array of strings containing the feature names of the <code>vaeac</code> model.</dd>
<dt>feature_names_new</dt>
<dd>Array of strings containing the feature names to compare with.</dd></dl>

Arguments

Author

Function that checks the feature names of data and vaeac model — vaeac_check_x_colnames

<dl>

<dt>feature_names_vaeac</dt>
<dd>Array of strings containing the feature names of the <code>vaeac</code> model.</dd>


<dt>feature_names_new</dt>
<dd>Array of strings containing the feature names to compare with.</dd>

</dl>

Function that checks the feature names of data and <code>vaeac</code> model

vaeac_check_x_colnames: Function that checks the feature names of data and `vaeac` model

Description

Usage

Value

Arguments

Author