  Ensembles of decision trees perform well on many problems, but are not
interpretable. In contrast to existing approaches in interpretability that
focus on explaining relationships between features and predictions, we propose
an alternative approach to interpret tree ensemble classifiers by surfacing
representative points for each class -- prototypes. We introduce a new distance
for Gradient Boosted Tree models, and propose new, adaptive prototype selection
methods with theoretical guarantees, with the flexibility to choose a different
number of prototypes in each class. We demonstrate our methods on random
forests and gradient boosted trees, showing that the prototypes can perform as
well as or even better than the original tree ensemble when used as a
nearest-prototype classifier. In a user study, humans were better at predicting
the output of a tree ensemble classifier when using prototypes than when using
Shapley values, a popular feature attribution method. Hence, prototypes present
a viable alternative to feature-based explanations for tree ensembles.
