iml-package

The iml package provides tools to analyze machine learning models and predictions.

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

iml-package function

Maintainer: Christoph Molnar <a href="/link/christoph.molnar%40gmail.com?package=iml&version=0.11.1" data-mini-rdoc="iml::christoph.molnar@gmail.com">christoph.molnar@gmail.com</a>
Authors:<ul>
<li>Patrick Schratz <a href="/link/patrick.schratz%40gmail.com?package=iml&version=0.11.1" data-mini-rdoc="iml::patrick.schratz@gmail.com">patrick.schratz@gmail.com</a> (<a href="https://orcid.org/0000-0003-0748-6624">ORCID</a>)</li>
</ul>

Author

Make machine learning models and predictions interpretable — iml-package

Maintainer: Christoph Molnar <a href='mailto:christoph.molnar@gmail.com'>christoph.molnar@gmail.com</a>
Authors:<ul>
<li>Patrick Schratz <a href='mailto:patrick.schratz@gmail.com'>patrick.schratz@gmail.com</a> (<a href='https://orcid.org/0000-0003-0748-6624'>ORCID</a>)</li>
</ul>

iml-package: Make machine learning models and predictions interpretable

Description

Arguments

Author

See Also