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iml: An R package for Interpretable Machine Learning

Molnar, Christoph

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  "description": "<p>Interpretability methods to analyze the behavior and predictions of any machine learning model.<br>\nImplemented methods are:</p>\n\n<ul>\n\t<li>Feature importance described by Fisher et al. (2018)&lt;arXiv:1801.01489&gt;</li>\n\t<li>Partial dependence plots described by Friedman (2001) &lt;;</li>\n\t<li>Individual conditional expectation (&#39;ice&#39;) plots described by Goldstein et al. (2013)&lt;doi:10.1080/10618600.2014.907095&gt;</li>\n\t<li>Local models (variant of &#39;lime&#39;) described by Ribeiro et. al (2016) &lt;arXiv:1602.04938&gt;</li>\n\t<li>Shapley Value described by Strumbelj et. al (2014) &lt;doi:10.1007/s10115-013-0679-x&gt;</li>\n\t<li>Feature interactions described by Friedman et. al &lt;doi:10.1214/07-AOAS148&gt;</li>\n\t<li>Tree surrogate models.</li>\n</ul>", 
  "license": "", 
  "creator": [
      "affiliation": "LMU Munich", 
      "@id": "", 
      "@type": "Person", 
      "name": "Molnar, Christoph"
  "url": "", 
  "datePublished": "2018-06-27", 
  "version": "v0.5.2", 
  "keywords": [
    "Interpretable Machine Learning", 
    "Machine Learning", 
  "@context": "", 
  "identifier": "", 
  "@id": "", 
  "@type": "SoftwareSourceCode", 
  "name": "iml: An R package for Interpretable Machine Learning"
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