vaeac_compute_normalization

Returns the means and standard deviations for all continuous features in the data set.
Categorical features get \(mean = 0\) and \(sd = 1\) by default.

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_compute_normalization function

<dl><dt>data</dt>
<dd>A torch_tensor of dimension <code>n_observation</code> x <code>n_features</code> containing the data.</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

Compute Featurewise Means and Standard Deviations — vaeac_compute_normalization

<dl>

<dt>data</dt>
<dd>A torch_tensor of dimension <code>n_observation</code> x <code>n_features</code> containing the data.</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>

Compute Featurewise Means and Standard Deviations

vaeac_compute_normalization: Compute Featurewise Means and Standard Deviations

Description

Usage

Value

Arguments

Author