vaeac_get_full_state_list

#' @description
The function extract the objects that we are going to save together with the <code>vaeac</code> model to make it possible to
train the model further and to evaluate it.
The environment should be the local environment inside the <code>vaeac_train_model_auxiliary()</code> function.

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_get_full_state_list function

<dl><dt>environment</dt>
<dd>The <code><a href="/link/base%3A%3Aenvironment()?package=shapr&version=1.0.4" data-mini-rdoc="shapr::base::environment()">base::environment()</a></code> where the objects are stored.</dd></dl>

Arguments

Author

Function that extracts the state list objects from the environment — vaeac_get_full_state_list

<dl>

<dt>environment</dt>
<dd>The <code><a href='https://rdrr.io/r/base/environment.html'>base::environment()</a></code> where the objects are stored.</dd>

</dl>

Function that extracts the state list objects from the environment

vaeac_get_full_state_list: Function that extracts the state list objects from the environment

Description

Usage

Value

Arguments

Author