calculate.ale.num.cat

Compute ALE for 2 features, one numerical, one categorical

internal

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.

Christoph Molnar

Interpretable Machine Learning

Patrick Schratz

calculate.ale.num.cat function

<dl><dt>dat</dt>
<dd>the data.frame with same columns as training data</dd>
<dt>run.prediction</dt>
<dd>Predict function of type: f(newdata)</dd>
<dt>feature.name</dt>
<dd>The column name of the features for which to compute ALE</dd>
<dt>grid.dt</dt>
<dd>data.table with single column with grid values for the numerical feature</dd></dl>

Arguments

Compute ALE for 2 features, one numerical, one categorical — calculate.ale.num.cat

<dl>

<dt>dat</dt>
<dd>the data.frame with same columns as training data</dd>


<dt>run.prediction</dt>
<dd>Predict function of type: f(newdata)</dd>


<dt>feature.name</dt>
<dd>The column name of the features for which to compute ALE</dd>


<dt>grid.dt</dt>
<dd>data.table with single column with grid values for the numerical feature</dd>

</dl>

calculate.ale.num.cat: Compute ALE for 2 features, one numerical, one categorical

Description

Usage

Arguments