vaeac_check_which_vaeac_model

Function that checks for valid <code>vaeac</code> model name

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_which_vaeac_model function

<dl><dt>which_vaeac_model</dt>
<dd>String (default is <code>best</code>). The name of the <code>vaeac</code> model (snapshots from different
epochs) to use when generating the Monte Carlo samples. The standard choices are: <code>"best"</code> (epoch with lowest IWAE),
<code>"best_running"</code> (epoch with lowest running IWAE, see <code>vaeac.running_avg_n_values</code>), and <code>last</code> (the last epoch).
Note that additional choices are available if <code>vaeac.save_every_nth_epoch</code> is provided. For example, if
<code>vaeac.save_every_nth_epoch = 5</code>, then <code>vaeac.which_vaeac_model</code> can also take the values <code>"epoch_5"</code>, <code>"epoch_10"</code>,
<code>"epoch_15"</code>, and so on.</dd>
<dt>epochs</dt>
<dd>Positive integer (default is <code>100</code>). The number of epochs to train the final vaeac model.
This includes <code>epochs_initiation_phase</code>, where the default is <code>2</code>.</dd>
<dt>save_every_nth_epoch</dt>
<dd>Positive integer (default is <code>NULL</code>). If provided, then the vaeac model after
every <code>save_every_nth_epoch</code>th epoch will be saved.</dd></dl>

Arguments

Author

Function that checks for valid vaeac model name — vaeac_check_which_vaeac_model

<dl>

<dt>which_vaeac_model</dt>
<dd>String (default is <code>best</code>). The name of the <code>vaeac</code> model (snapshots from different
epochs) to use when generating the Monte Carlo samples. The standard choices are: <code>"best"</code> (epoch with lowest IWAE),
<code>"best_running"</code> (epoch with lowest running IWAE, see <code>vaeac.running_avg_n_values</code>), and <code>last</code> (the last epoch).
Note that additional choices are available if <code>vaeac.save_every_nth_epoch</code> is provided. For example, if
<code>vaeac.save_every_nth_epoch = 5</code>, then <code>vaeac.which_vaeac_model</code> can also take the values <code>"epoch_5"</code>, <code>"epoch_10"</code>,
<code>"epoch_15"</code>, and so on.</dd>


<dt>epochs</dt>
<dd>Positive integer (default is <code>100</code>). The number of epochs to train the final vaeac model.
This includes <code>epochs_initiation_phase</code>, where the default is <code>2</code>.</dd>


<dt>save_every_nth_epoch</dt>
<dd>Positive integer (default is <code>NULL</code>). If provided, then the vaeac model after
every <code>save_every_nth_epoch</code>th epoch will be saved.</dd>

</dl>

Function that checks for valid <code>vaeac</code> model name

vaeac_check_which_vaeac_model: Function that checks for valid `vaeac` model name

Description

Usage

Value

Arguments

Author