Interpretable Machine Learning

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) , accumulated local effects plots described by Apley (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.