most_important

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) <doi:10.48550/arXiv.1612.08468>), further effects plots,
interaction strength, and variable contribution breakdown (Gosiewska
and Biecek (2019) <doi:10.48550/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.</dd></dl>

Arguments

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.</dd>

</dl>

most_important: Most Important Variables.

Description

Usage

Value

Arguments

See Also

Examples