PPimportance

Calculate the importance of variables in the <code>PPTreereg</code> model.
For local importance, weighted sum of projection coefficients with the number of data corresponding to
each node as the weighted value in each node is used.
The global importance is absolute sum of local importance.

It was developed as a tool for exploring 'PPTreereg' (Projection Pursuit TREE of REGression).
It uses various projection pursuit indexes and 'XAI' (eXplainable Artificial Intelligence) methods to help
understand the model by finding connections between the input variables and prediction values of the model.
The 'KernelSHAP' (Aas, Jullum and Løland (2019) <arXiv:1903.10464>) algorithm was modified to fit ‘PPTreereg’,
and some codes were modified from the 'shapr' package (Sellereite, Nikolai, and Martin Jullum (2020) <doi:10.21105/joss.02027>).
The implemented methods help to explore the model at the single instance level as well as at the whole dataset level.
Users can compare with other machine learning models by applying it to the 'DALEX' package of 'R'.

HyunSun Cho

PPtreeregViz

Projection Pursuit Regression Tree Visualization

Eun-Kyung Lee

Nikolai Sellereite

Martin Jullum

Annabelle Redelmeier

Norsk Regnesentral 

PPimportance function

<dl><dt>PPTreeregOBJ</dt>
<dd>PPTreereg class object - a model to be explained</dd>
<dt>...</dt>
<dd>arguments to be passed to methods</dd></dl>

Arguments

Calculate variable importance — PPimportance

<dl>

<dt>PPTreeregOBJ</dt>
<dd>PPTreereg class object - a model to be explained</dd>


<dt>...</dt>
<dd>arguments to be passed to methods</dd>

</dl>

PPimportance: Calculate variable importance

Description

Usage

Value

Arguments

Examples