get_formula

An appropriate fitted model object.

object

Calls <code><a href="/link/formula?package=vip&version=0.3.2" data-mini-rdoc="vip::formula">formula</a></code> to extract the formulae from various
modeling objects, but returns <code>NULL</code> instead of an error for objects
that do not contain a formula component.

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

Bernie Gray

get_formula function

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

Arguments

Calls <code><a href='https://rdrr.io/r/stats/formula.html'>formula</a></code> to extract the formulae from various
modeling objects, but returns <code>NULL</code> instead of an error for objects
that do not contain a formula component.

Extract model formula — get_formula

<dl>

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

</dl>

get_formula: Extract model formula

Description

Usage

Value

Arguments