vaeac_postprocess_data

vaeac generates numerical values. This function converts categorical features
to from numerics with class labels 1,2,...,K, to factors with the original and class labels.

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_postprocess_data function

<dl><dt>data</dt>
<dd>data.table containing the data generated by a vaeac model</dd>
<dt>vaeac_model_state_list</dt>
<dd>List. The returned list from the <code>vaeac_preprocess_data</code> function or
a loaded checkpoint list of a saved vaeac object.</dd></dl>

Arguments

Author

Postprocess Data Generated by a vaeac Model — vaeac_postprocess_data

<dl>

<dt>data</dt>
<dd>data.table containing the data generated by a vaeac model</dd>


<dt>vaeac_model_state_list</dt>
<dd>List. The returned list from the <code>vaeac_preprocess_data</code> function or
a loaded checkpoint list of a saved vaeac object.</dd>

</dl>

Postprocess Data Generated by a vaeac Model

vaeac_postprocess_data: Postprocess Data Generated by a vaeac Model

Description

Usage

Value

Arguments

Author