Software Open Access

iml: An R package for Interpretable Machine Learning

Molnar, Christoph

JSON Export

  "files": [
      "links": {
        "self": ""
      "checksum": "md5:de10f17f47d895bef3df78cfa62893ce", 
      "bucket": "8c1c0f13-485f-442e-9cd0-c4c6567eca3c", 
      "key": "iml-JOSS.tar.gz", 
      "type": "gz", 
      "size": 537520
  "owners": [
  "doi": "10.5281/zenodo.1299059", 
  "stats": {
    "version_unique_downloads": 9.0, 
    "unique_views": 237.0, 
    "views": 243.0, 
    "downloads": 9.0, 
    "unique_downloads": 9.0, 
    "version_unique_views": 237.0, 
    "volume": 4837680.0, 
    "version_downloads": 9.0, 
    "version_views": 243.0, 
    "version_volume": 4837680.0
  "links": {
    "doi": "", 
    "conceptdoi": "", 
    "bucket": "", 
    "conceptbadge": "", 
    "html": "", 
    "latest_html": "", 
    "badge": "", 
    "latest": ""
  "conceptdoi": "10.5281/zenodo.1299058", 
  "created": "2018-06-27T09:37:03.983852+00:00", 
  "updated": "2019-04-09T14:00:52.106581+00:00", 
  "conceptrecid": "1299058", 
  "revision": 4, 
  "id": 1299059, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.1299059", 
    "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": {
      "id": "CC-BY-4.0"
    "title": "iml: An R package for Interpretable Machine Learning", 
    "relations": {
      "version": [
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "1299058"
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "1299059"
    "version": "v0.5.2", 
    "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."
    "keywords": [
      "Interpretable Machine Learning", 
      "Machine Learning", 
    "publication_date": "2018-06-27", 
    "creators": [
        "orcid": "0000-0003-2331-868X", 
        "affiliation": "LMU Munich", 
        "name": "Molnar, Christoph"
    "access_right": "open", 
    "resource_type": {
      "type": "software", 
      "title": "Software"
    "related_identifiers": [
        "scheme": "doi", 
        "relation": "isVersionOf", 
        "identifier": "10.5281/zenodo.1299058"
All versions This version
Views 243243
Downloads 99
Data volume 4.8 MB4.8 MB
Unique views 237237
Unique downloads 99


Cite as