extract.glmnet.effects

Interpretability methods to analyze the behavior and
predictions of any machine learning model. Implemented methods are:
Feature importance described by Fisher et al. (2018)
<arXiv:1801.01489>, accumulated local effects plots described by Apley
(2018) <arXiv:1612.08468>, partial dependence plots described by
Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional
expectation ('ice') plots described by Goldstein et al. (2013)
<doi:10.1080/10618600.2014.907095>, local models (variant of 'lime')
described by Ribeiro et. al (2016) <arXiv:1602.04938>, the Shapley
Value described by Strumbelj et. al (2014)
<doi:10.1007/s10115-013-0679-x>, feature interactions described by
Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.

Giuseppe Casalicchio

Interpretable Machine Learning

Christoph Molnar

Patrick Schratz

extract.glmnet.effects function

<dl><dt>betas</dt>
<dd>glmnet$beta</dd>
<dt>best.index</dt>
<dd>index k</dd>
<dt>x.recoded</dt>
<dd>the recoded version of x</dd>
<dt>x.original</dt>
<dd>the original x
Assuming that the first row is the x.interest</dd></dl>

Arguments

Extract glmnet effects — extract.glmnet.effects

<dl>

<dt>betas</dt>
<dd>glmnet$beta</dd>


<dt>best.index</dt>
<dd>index k</dd>


<dt>x.recoded</dt>
<dd>the recoded version of x</dd>


<dt>x.original</dt>
<dd>the original x
Assuming that the first row is the x.interest</dd>

</dl>

extract.glmnet.effects: Extract glmnet effects

Description

Usage

Arguments