cli_startup

Printing startup messages with cli

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 

cli_startup function

<dl><dt>internal</dt>
<dd>List.
Not used directly, but passed through from <code>explain()</code>.</dd>
<dt>model_class</dt>
<dd>String.
Class of the model as a string</dd>
<dt>verbose</dt>
<dd>String vector or NULL.
Specifies the verbosity (printout detail level) through one or more of strings <code>"basic"</code>, <code>"progress"</code>,
<code>"convergence"</code>, <code>"shapley"</code> and <code>"vS_details"</code>.
<code>"basic"</code> (default) displays basic information about the computation which is being performed,
in addition to some messages about parameters being sets or checks being unavailable due to specific input.
<code>"progress</code> displays information about where in the calculation process the function currently is.
#' <code>"convergence"</code> displays information on how close to convergence the Shapley value estimates are
(only when <code>iterative = TRUE</code>) .
<code>"shapley"</code> displays intermediate Shapley value estimates and standard deviations (only when <code>iterative = TRUE</code>)
and the final estimates.
<code>"vS_details"</code> displays information about the v_S estimates.
This is most relevant for <code>approach %in% c("regression_separate", "regression_surrogate", "vaeac"</code>).
<code>NULL</code> means no printout.
Note that any combination of four strings can be used.
E.g. <code>verbose = c("basic", "vS_details")</code> will display basic information + details about the v(S)-estimation process.</dd></dl>

Arguments

Printing startup messages with cli — cli_startup

<dl>

<dt>internal</dt>
<dd>List.
Not used directly, but passed through from <code>explain()</code>.</dd>


<dt>model_class</dt>
<dd>String.
Class of the model as a string</dd>


<dt>verbose</dt>
<dd>String vector or NULL.
Specifies the verbosity (printout detail level) through one or more of strings <code>"basic"</code>, <code>"progress"</code>,
<code>"convergence"</code>, <code>"shapley"</code> and <code>"vS_details"</code>.
<code>"basic"</code> (default) displays basic information about the computation which is being performed,
in addition to some messages about parameters being sets or checks being unavailable due to specific input.
<code>"progress</code> displays information about where in the calculation process the function currently is.
#' <code>"convergence"</code> displays information on how close to convergence the Shapley value estimates are
(only when <code>iterative = TRUE</code>) .
<code>"shapley"</code> displays intermediate Shapley value estimates and standard deviations (only when <code>iterative = TRUE</code>)
and the final estimates.
<code>"vS_details"</code> displays information about the v_S estimates.
This is most relevant for <code>approach %in% c("regression_separate", "regression_surrogate", "vaeac"</code>).
<code>NULL</code> means no printout.
Note that any combination of four strings can be used.
E.g. <code>verbose = c("basic", "vS_details")</code> will display basic information + details about the v(S)-estimation process.</dd>

</dl>

Printing startup messages with cli

cli_startup: Printing startup messages with cli

Description

Usage

Value

Arguments