gen_friedman

Integer specifying the number of samples (i.e., rows) to
generate. Default is 100.

n_samples

Integer specifying the number of features to generate.
Default is 10.

n_features

Integer specifying the number of (roughly) equal sized bins to
split the response into. Default is <code>NULL</code> for no binning. Setting to
a positive integer &gt; 1 effectively turns this into a classification problem
where <code>n_bins</code> gives the number of classes.

n_bins

Numeric specifying the standard deviation of the noise.

sigma

Integer specifying the random seed. If <code>NULL</code> (the default)
the results will be different each time the function is run.

seed

Simulate data from the Friedman 1 benchmark problem. See
<code>mlbench.friedman1</code> for details and references.

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

gen_friedman function

<dl><dt>n_samples</dt>
<dd>Integer specifying the number of samples (i.e., rows) to
generate. Default is 100.</dd>
<dt>n_features</dt>
<dd>Integer specifying the number of features to generate.
Default is 10.</dd>
<dt>n_bins</dt>
<dd>Integer specifying the number of (roughly) equal sized bins to
split the response into. Default is <code>NULL</code> for no binning. Setting to
a positive integer &gt; 1 effectively turns this into a classification problem
where <code>n_bins</code> gives the number of classes.</dd>
<dt>sigma</dt>
<dd>Numeric specifying the standard deviation of the noise.</dd>
<dt>seed</dt>
<dd>Integer specifying the random seed. If <code>NULL</code> (the default)
the results will be different each time the function is run.</dd></dl>

Arguments

Friedman benchmark data — gen_friedman

<dl>

<dt>n_samples</dt>
<dd>Integer specifying the number of samples (i.e., rows) to
generate. Default is 100.</dd>


<dt>n_features</dt>
<dd>Integer specifying the number of features to generate.
Default is 10.</dd>


<dt>n_bins</dt>
<dd>Integer specifying the number of (roughly) equal sized bins to
split the response into. Default is <code>NULL</code> for no binning. Setting to
a positive integer &gt; 1 effectively turns this into a classification problem
where <code>n_bins</code> gives the number of classes.</dd>


<dt>sigma</dt>
<dd>Numeric specifying the standard deviation of the noise.</dd>


<dt>seed</dt>
<dd>Integer specifying the random seed. If <code>NULL</code> (the default)
the results will be different each time the function is run.</dd>

</dl>

gen_friedman: Friedman benchmark data

Description

Usage

Arguments

Examples