vaeac_check_masking_ratio

Function that checks that the masking ratio argument is valid

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_check_masking_ratio function

<dl><dt>masking_ratio</dt>
<dd>Numeric (default is <code>0.5</code>). Probability of masking a feature in the
<code>mcar_mask_generator()</code> (MCAR = Missing Completely At Random). The MCAR masking scheme ensures that <code>vaeac</code>
model can do arbitrary conditioning as all coalitions will be trained. <code>masking_ratio</code> will be overruled if
<code>mask_gen_coalitions</code> is specified.</dd>
<dt>n_features</dt>
<dd>The number of features, i.e., the number of columns in the training data.</dd></dl>

Arguments

Author

Function that checks that the masking ratio argument is valid — vaeac_check_masking_ratio

<dl>

<dt>masking_ratio</dt>
<dd>Numeric (default is <code>0.5</code>). Probability of masking a feature in the
<code>mcar_mask_generator()</code> (MCAR = Missing Completely At Random). The MCAR masking scheme ensures that <code>vaeac</code>
model can do arbitrary conditioning as all coalitions will be trained. <code>masking_ratio</code> will be overruled if
<code>mask_gen_coalitions</code> is specified.</dd>


<dt>n_features</dt>
<dd>The number of features, i.e., the number of columns in the training data.</dd>

</dl>

Function that checks that the masking ratio argument is valid

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vaeac_check_masking_ratio: Function that checks that the masking ratio argument is valid

Description

Usage

Value

Arguments

Author