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).

Variable Importance Plots

package-ubuntu-20.04-r-4.0.5#cran#vip-0.3.2

<p>Extract predictor names from a fitted model.
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>object</code></td>
<td>
<p>An appropriate fitted model object.</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>Additional optional arguments.</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#vip-0.3.2#docs#get_feature_names

get_feature_names

Description

Usage

Arguments

vip

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`object`	An appropriate fitted model object.
`...`	Additional optional arguments.