45 citations · 61 across the 4 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
stat.ML2021★ 16 cited
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects a…
stat.ML2021
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Clément Bénard, Sébastien da Veiga, Erwan Scornet
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests. Although the mean decrease accuracy (MDA) is widely accepted as the most effi…