calculate.ale.num.cat

the data.frame with same columns as training data

Predict function of type: f(newdata)

run.prediction

The column name of the features for which to compute ALE

feature.name

The number of intervals for the numerical feature

grid.size

Compute ALE for 2 features, one numerical, one categorical

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) <http://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.

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

Description

Usage

Arguments