print.Partitioning

Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.

Holger Löwe

fmeffects

Model-Agnostic Interpretations with Forward Marginal Effects

Christian Scholbeck

Christian Heumann

Bernd Bischl

Giuseppe Casalicchio

print.Partitioning function

<dl><dt>x</dt>
<dd>object of class <code>Partitioning</code>.</dd>
<dt>...</dt>
<dd>additional arguments affecting the summary produced.</dd></dl>

Arguments

Prints an FME Partitioning. — print.Partitioning

<dl>

<dt>x</dt>
<dd>object of class <code>Partitioning</code>.</dd>


<dt>...</dt>
<dd>additional arguments affecting the summary produced.</dd>

</dl>

Prints an FME Partitioning.

print.Partitioning: Prints an FME Partitioning.

Description

Usage

Arguments