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iml: An R package for Interpretable Machine Learning

Molnar, Christoph


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    <subfield code="a">iml: An R package for Interpretable Machine Learning</subfield>
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    <subfield code="a">&lt;p&gt;Interpretability methods to analyze the behavior and predictions of any machine learning model.&lt;br&gt;
Implemented methods are:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Feature importance described by Fisher et al. (2018)&amp;lt;arXiv:1801.01489&amp;gt;&lt;/li&gt;
	&lt;li&gt;Partial dependence plots described by Friedman (2001) &amp;lt;http://www.jstor.org/stable/2699986&amp;gt;&lt;/li&gt;
	&lt;li&gt;Individual conditional expectation (&amp;#39;ice&amp;#39;) plots described by Goldstein et al. (2013)&amp;lt;doi:10.1080/10618600.2014.907095&amp;gt;&lt;/li&gt;
	&lt;li&gt;Local models (variant of &amp;#39;lime&amp;#39;) described by Ribeiro et. al (2016) &amp;lt;arXiv:1602.04938&amp;gt;&lt;/li&gt;
	&lt;li&gt;Shapley Value described by Strumbelj et. al (2014) &amp;lt;doi:10.1007/s10115-013-0679-x&amp;gt;&lt;/li&gt;
	&lt;li&gt;Feature interactions described by Friedman et. al &amp;lt;doi:10.1214/07-AOAS148&amp;gt;&lt;/li&gt;
	&lt;li&gt;Tree surrogate models.&lt;/li&gt;
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