6 citations · 9 across the 12 of their papers we have counts for
3 papers · 1 filter
Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models
Salim I. Amoukou, Nicolas J. B Brunel
To explain the decision of any model, we extend the notion of probabilistic Sufficient Explanations (P-SE). For each instance, this approach selects the minimal subset of features…
Accurate Shapley Values for explaining tree-based models
Salim I. Amoukou, Nicolas J-B. Brunel, Tangi Salaün
Shapley Values (SV) are widely used in explainable AI, but their estimation and interpretation can be challenging, leading to inaccurate inferences and explanations. As a starting…
The Shapley Value of coalition of variables provides better explanations
Salim I. Amoukou, Nicolas J-B. Brunel, Tangi Salaün
While Shapley Values (SV) are one of the gold standard for interpreting machine learning models, we show that they are still poorly understood, in particular in the presence of cat…