  SHAP (SHapley Additive exPlanation) values are one of the leading tools for
interpreting machine learning models, with strong theoretical guarantees
(consistency, local accuracy) and a wide availability of implementations and
use cases. Even though computing SHAP values takes exponential time in general,
TreeSHAP takes polynomial time on tree-based models. While the speedup is
significant, TreeSHAP can still dominate the computation time of industry-level
machine learning solutions on datasets with millions or more entries, causing
delays in post-hoc model diagnosis and interpretation service. In this paper we
present two new algorithms, Fast TreeSHAP v1 and v2, designed to improve the
computational efficiency of TreeSHAP for large datasets. We empirically find
that Fast TreeSHAP v1 is 1.5x faster than TreeSHAP while keeping the memory
cost unchanged. Similarly, Fast TreeSHAP v2 is 2.5x faster than TreeSHAP, at
the cost of a slightly higher memory usage, thanks to the pre-computation of
expensive TreeSHAP steps. We also show that Fast TreeSHAP v2 is well-suited for
multi-time model interpretations, resulting in as high as 3x faster explanation
of newly incoming samples.
