vaeac_dataset

Convert a the data into a <code>torch::dataset()</code> which the vaeac model creates batches from.

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_dataset function

<dl><dt>X</dt>
<dd>A torch_tensor contain the data of shape N x p, where N and p are the number
of observations and features, respectively.</dd>
<dt>one_hot_max_sizes</dt>
<dd>A torch tensor of dimension <code>n_features</code> containing the one hot sizes of the <code>n_features</code>
features. That is, if the <code>i</code>th feature is a categorical feature with 5 levels, then <code>one_hot_max_sizes[i] = 5</code>.
While the size for continuous features can either be <code>0</code> or <code>1</code>.</dd></dl>

Arguments

Author

Dataset used by the vaeac model — vaeac_dataset

<dl>

<dt>X</dt>
<dd>A torch_tensor contain the data of shape N x p, where N and p are the number
of observations and features, respectively.</dd>


<dt>one_hot_max_sizes</dt>
<dd>A torch tensor of dimension <code>n_features</code> containing the one hot sizes of the <code>n_features</code>
features. That is, if the <code>i</code>th feature is a categorical feature with 5 levels, then <code>one_hot_max_sizes[i] = 5</code>.
While the size for continuous features can either be <code>0</code> or <code>1</code>.</dd>

</dl>

Dataset used by the <code>vaeac</code> model

vaeac_dataset: Dataset used by the `vaeac` model

Description

Usage

Arguments

Author

Details