  The original development of Shapley values for prediction explanation relied
on the assumption that the features being described were independent. If the
features in reality are dependent this may lead to incorrect explanations.
Hence, there have recently been attempts of appropriately modelling/estimating
the dependence between the features. Although the proposed methods clearly
outperform the traditional approach assuming independence, they have their
weaknesses. In this paper we propose two new approaches for modelling the
dependence between the features.
  Both approaches are based on vine copulas, which are flexible tools for
modelling multivariate non-Gaussian distributions able to characterise a wide
range of complex dependencies.
  The performance of the proposed methods is evaluated on simulated data sets
and a real data set. The experiments demonstrate that the vine copula
approaches give more accurate approximations to the true Shapley values than
its competitors.
