most_important

most_important.default

most_important.light_importance

Returns the most important variable names sorted descendingly.

Shed light on black box machine learning models by the help
of model performance, variable importance, global surrogate models,
ICE profiles, partial dependence (Friedman J. H. (2001)
<doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W.
(2016) <arXiv:1612.08468>), further effects plots, interaction
strength, and variable contribution breakdown (Gosiewska and Biecek
(2019) <arxiv:1903.11420>). All tools are implemented to work with
case weights and allow for stratified analysis. Furthermore, multiple
flashlights can be combined and analyzed together.

Michael Mayer

flashlight

Shed Light on Black Box Machine Learning Models

most_important function

<dl><dt>x</dt>
<dd>An object of class "light_importance".</dd>
<dt>top_m</dt>
<dd>Maximum number of important variables to be returned.
Defaults to <code>Inf</code>, i.e., return all variables in descending order of importance.</dd></dl>

Arguments

<ul>
<li><code>most_important(default)</code>: Default method not implemented yet.</li>
<li><code>most_important(light_importance)</code>: Extracts most important variables from an object of class
"light_importance".</li>
</ul>

Methods (by class)

Most Important Variables. — most_important

<dl>

<dt>x</dt>
<dd>An object of class "light_importance".</dd>


<dt>top_m</dt>
<dd>Maximum number of important variables to be returned.
Defaults to <code>Inf</code>, i.e., return all variables in descending order of importance.</dd>

</dl>

most_important: Most Important Variables.

Description

Usage

Value

Arguments

Methods (by class)

See Also

Examples