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iml: An R package for Interpretable Machine Learning

Molnar, Christoph

Interpretability methods to analyze the behavior and predictions of any machine learning model.
Implemented methods are:

  • Feature importance described by Fisher et al. (2018)<arXiv:1801.01489>
  • Partial dependence plots described by Friedman (2001) <>
  • Individual conditional expectation ('ice') plots described by Goldstein et al. (2013)<doi:10.1080/10618600.2014.907095>
  • Local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>
  • Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>
  • Feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148>
  • Tree surrogate models.

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