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It is becoming increasingly important to explain complex, black-box machinelearning models. Although there is an expanding literature on this topic,Shapley values stand out as a sound method to explain predictions from any typeof machine learning model. The original development of Shapley values forprediction explanation relied on the assumption that the features beingdescribed were independent. This methodology was then extended to explaindependent features with an underlying continuous distribution. In this paper,we propose a method to explain mixed (i.e. continuous, discrete, ordinal, andcategorical) dependent features by modeling the dependence structure of thefeatures using conditional inference trees. We demonstrate our proposed methodagainst the current industry standards in various simulation studies and findthat our method often outperforms the other approaches. Finally, we apply ourmethod to a real financial data set used in the 2018 FICO Explainable MachineLearning Challenge and show how our explanations compare to the FICO challengeRecognition Award winning team.

Explaining predictive models with mixed features using Shapley values and conditional inference trees