  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 categorical variables or of variables of low
importance. For instance, we show that the popular practice that consists in
summing the SV of dummy variables is false as it provides wrong estimates of
all the SV in the model and implies spurious interpretations. Based on the
identification of null and active coalitions, and a coalitional version of the
SV, we provide a correct computation and inference of important variables.
Moreover, a Python library (All the experiments and simulations can be
reproduced with the publicly available library Active Coalition of Variables,
https://www.github.com/salimamoukou/acv00) that computes reliably conditional
expectations and SV for tree-based models, is implemented and compared with
state-of-the-art algorithms on toy models and real data sets.
