vaeac_save_state

Function that saves the state list and the current save state of the <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_save_state function

<dl><dt>state_list</dt>
<dd>List containing all the parameters in the state.</dd>
<dt>file_name</dt>
<dd>String containing the file path.</dd>
<dt>return_state</dt>
<dd>Logical if we are to return the state list or not.</dd></dl>

Arguments

Author

Function that saves the state list and the current save state of the vaeac model — vaeac_save_state

<dl>

<dt>state_list</dt>
<dd>List containing all the parameters in the state.</dd>


<dt>file_name</dt>
<dd>String containing the file path.</dd>


<dt>return_state</dt>
<dd>Logical if we are to return the state list or not.</dd>

</dl>

Function that saves the state list and the current save state of the <code>vaeac</code> model

vaeac_save_state: Function that saves the state list and the current save state of the `vaeac` model

Description

Usage

Value

Arguments

Author