get_feature_names

Extract predictor names from a fitted model.

internal

A general framework for constructing variable importance plots from
various types of machine learning models in R. Aside from some standard model-
specific variable importance measures, this package also provides model-
agnostic approaches that can be applied to any supervised learning algorithm.
These include 1) an efficient permutation-based variable importance measure,
2) variable importance based on Shapley values (Strumbelj and Kononenko,
2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based
approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A
variance-based method for quantifying the relative strength of interaction
effects is also included (see the previous reference for details).

Brandon Greenwell

Variable Importance Plots

Brad Boehmke

get_feature_names function

<dl><dt>object</dt>
<dd>An appropriate fitted model object.</dd>
<dt>...</dt>
<dd>Additional optional arguments.</dd></dl>

Arguments

Extract feature names — get_feature_names

<dl>

<dt>object</dt>
<dd>An appropriate fitted model object.</dd>


<dt>...</dt>
<dd>Additional optional arguments.</dd>

</dl>

get_feature_names: Extract feature names

Description

Usage

Arguments