iml

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) doi:10.48550/arxiv.1801.01489, accumulated local effects plots described by Apley (2018) doi:10.48550/arxiv.1612.08468, partial dependence plots described by Friedman (2001) www.jstor.org/stable/2699986, 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) doi:10.48550/arXiv.1602.04938, the 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 and tree surrogate models.

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