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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;http://www.jstor.org/stable/2699986&gt;</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": {
      "id": "CC-BY-4.0"
    }, 
    "title": "iml: An R package for Interpretable Machine Learning", 
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    "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."
    ], 
    "keywords": [
      "Interpretable Machine Learning", 
      "Machine Learning", 
      "R"
    ], 
    "publication_date": "2018-06-27", 
    "creators": [
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        "orcid": "0000-0003-2331-868X", 
        "affiliation": "LMU Munich", 
        "name": "Molnar, Christoph"
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