iml: Interpretable Machine Learning
Version 0.5.1

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) , partial dependence plots described by Friedman (2001) , individual conditional expectation ('ice') plots described by Goldstein et al. (2013) , local models (variant of 'lime') described by Ribeiro et. al (2016) , the Shapley Value described by Strumbelj et. al (2014) , feature interactions described by Friedman et. al and tree surrogate models.

Package details

AuthorChristoph Molnar [aut, cre]
Date of publication2018-05-15 07:36:07 UTC
MaintainerChristoph Molnar <[email protected]>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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iml documentation built on May 15, 2018, 9:04 a.m.