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Explaining complex or seemingly simple machine learning models is animportant practical problem. We want to explain individual predictions from acomplex machine learning model by learning simple, interpretable explanations.Shapley values is a game theoretic concept that can be used for this purpose.The Shapley value framework has a series of desirable theoretical properties,and can in principle handle any predictive model. Kernel SHAP is acomputationally efficient approximation to Shapley values in higher dimensions.Like several other existing methods, this approach assumes that the featuresare independent, which may give very wrong explanations. This is the case evenif a simple linear model is used for predictions. In this paper, we extend theKernel SHAP method to handle dependent features. We provide several examples oflinear and non-linear models with various degrees of feature dependence, whereour method gives more accurate approximations to the true Shapley values. Wealso propose a method for aggregating individual Shapley values, such that theprediction can be explained by groups of dependent variables.

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values