SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. (arXiv:2105.11724v3 [stat.ML] UPDATED)

Interpretability of learning algorithms is crucial for applications involving
critical decisions, and variable importance is one of the main interpretation
tools. Shapley effects are now widely used to interpret both tree ensembles and
neural networks, as they can efficiently handle dependence and interactions in
the data, as opposed to most other variable importance measures. However,
estimating Shapley effects is a challenging task, because of the computational
complexity and the conditional expectation estimates. Accordingly, existing
Shapley algorithms have flaws: a costly …

arxiv ml random random forests