  Additive feature explanations using Shapley values have become popular for
providing transparency into the relative importance of each feature to an
individual prediction of a machine learning model. While Shapley values provide
a unique additive feature attribution in cooperative game theory, the Shapley
values that can be generated for even a single machine learning model are far
from unique, with theoretical and implementational decisions affecting the
resulting attributions. Here, we consider the application of Shapley values for
explaining decision tree ensembles and present a novel approach to Shapley
value-based feature attribution that can be applied to random forests and
boosted decision trees. This new method provides attributions that accurately
reflect details of the model prediction algorithm for individual instances,
while being computationally competitive with one of the most widely used
current methods. We explain the theoretical differences between the standard
and novel approaches and compare their performance using synthetic and real
data.
