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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>
  <creators>
    <creator>
      <creatorName>Molnar, Christoph</creatorName>
      <givenName>Christoph</givenName>
      <familyName>Molnar</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2331-868X</nameIdentifier>
      <affiliation>LMU Munich</affiliation>
    </creator>
  </creators>
  <titles>
    <title>iml: An R package for Interpretable Machine Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Interpretable Machine Learning</subject>
    <subject>Machine Learning</subject>
    <subject>R</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-06-27</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1299059</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1299058</relatedIdentifier>
  </relatedIdentifiers>
  <version>v0.5.2</version>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <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;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;
&lt;/ul&gt;</description>
    <description descriptionType="Other">{"references": ["Biecek, Przemyslaw. 2018. DALEX: Descriptive mAchine Learning Explanations. https: //CRAN.R-project.org/package=DALEX.a", "Choudhary, Pramit, Aaron Kramer, and contributors datascience.com team. 2018. \"Skater: Model Interpretation Library.\" https://doi.org/10.5281/zenodo.1198885.", "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.\" http://arxiv.org/abs/1801.01489.", "Friedman, Jerome H. 2001. \"Greedy Function Approximation: A Gradient Boosting Ma- chine.\" Annals of Statistics. JSTOR, 1189\u20131232. https://doi.org/10.1214/aos/1013203451.", "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. https://doi.org/10.1214/07-AOAS148.", "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. https://doi.org/10.1080/10618600.2014.907095.", "Greenwell, Brandon M. 2017. \"Pdp: An R Package for Constructing Partial Depen- dence Plots.\" The R Journal 9 (1):421\u201336. https://journal.r-project.org/archive/2017/ RJ-2017-016/index.html.", "Pedersen, Thomas Lin, and Micha\u00ebl Benesty. 2017. Lime: Local Interpretable Model- Agnostic Explanations. https://CRAN.R-project.org/package=lime.", "R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.", "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. https://doi.org/10.1145/2939672.2939778.", "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. https://doi.org/10.1007/s10115-013-0679-x."]}</description>
  </descriptions>
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