  Decision trees are well-known due to their ease of interpretability. To
improve accuracy, we need to grow deep trees or ensembles of trees. These are
hard to interpret, offsetting their original benefits. Shapley values have
recently become a popular way to explain the predictions of tree-based machine
learning models. It provides a linear weighting to features independent of the
tree structure. The rise in popularity is mainly due to TreeShap, which solves
a general exponential complexity problem in polynomial time. Following
extensive adoption in the industry, more efficient algorithms are required.
This paper presents a more efficient and straightforward algorithm: Linear
TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount
of memory.
