iml-package

<p>The iml package provides tools to analyse machine learning models and predictions.</p>

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>,
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.

iml-package: Make machine learning models and predictions interpretable

Description

Arguments

See Also