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iml: An R package for Interpretable Machine Learning

Molnar, Christoph

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  <identifier identifierType="DOI">10.5281/zenodo.1299059</identifier>
      <creatorName>Molnar, Christoph</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0003-2331-868X</nameIdentifier>
      <affiliation>LMU Munich</affiliation>
    <title>iml: An R package for Interpretable Machine Learning</title>
    <subject>Interpretable Machine Learning</subject>
    <subject>Machine Learning</subject>
    <date dateType="Issued">2018-06-27</date>
  <resourceType resourceTypeGeneral="Software"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1299058</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&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;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;;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;
    <description descriptionType="Other">{"references": ["Biecek, Przemyslaw. 2018. DALEX: Descriptive mAchine Learning Explanations. https: //", "Choudhary, Pramit, Aaron Kramer, and contributors team. 2018. \"Skater: Model Interpretation Library.\"", "Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. 2018. \"Model Class Re- liance: Variable Importance Measures for any Machine Learning Model Class, from the \"Rashomon\" Perspective.\"", "Friedman, Jerome H. 2001. \"Greedy Function Approximation: A Gradient Boosting Ma- chine.\" Annals of Statistics. JSTOR, 1189\u20131232.", "Friedman, Jerome H, Bogdan E Popescu, and others. 2008. \"Predictive Learning via Rule Ensembles.\" The Annals of Applied Statistics 2 (3). Institute of Mathematical Statistics:916\u201354.", "Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2015. \"Peeking In- side the Black Box: Visualizing Statistical Learning with Plots of Individual Condi- tional Expectation.\" Journal of Computational and Graphical Statistics 24 (1):44\u201365.", "Greenwell, Brandon M. 2017. \"Pdp: An R Package for Constructing Partial Depen- dence Plots.\" The R Journal 9 (1):421\u201336. RJ-2017-016/index.html.", "Pedersen, Thomas Lin, and Micha\u00ebl Benesty. 2017. Lime: Local Interpretable Model- Agnostic Explanations.", "R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.", "Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. \"Why Should I Trust You?: Explaining the Predictions of Any Classifier.\" In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 1135\u201344. ACM.", "Strumbelj, Erik, Igor Kononenko, Erik \u0160trumbelj, and Igor Kononenko. 2014. \"Explain- ing prediction models and individual predictions with feature contributions.\" Knowledge and Information Systems 41 (3):647\u201365."]}</description>
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