8 citations · 14 across the 18 of their papers we have counts for
3 papers · 1 filter
VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution
Mateusz Gajewski, Mikołaj Morzy, Adam Karczmarz +1
Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnos…
Accurate estimation of feature importance faithfulness for tree models
Mateusz Gajewski, Adam Karczmarz, Mateusz Rapicki +1
In this paper, we consider a perturbation-based metric of predictive faithfulness of feature rankings (or attributions) that we call PGI squared. When applied to decision tree-base…
Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf
Adam Karczmarz, Anish Mukherjee, Piotr Sankowski +1
Shapley values are one of the main tools used to explain predictions of tree ensemble models. The main alternative to Shapley values are Banzhaf values that have not been understoo…