Linear TreeShap
This work addresses the need for faster Shapley value computation in industry applications, representing an incremental improvement over existing methods.
The paper tackles the computational inefficiency of TreeShap for explaining tree-based models by introducing Linear TreeShap, a more efficient and straightforward algorithm that maintains exactness and memory requirements.
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.