  Recent years have witnessed a fast-growing interest in computing explanations
for Machine Learning (ML) models predictions. For non-interpretable ML models,
the most commonly used approaches for computing explanations are heuristic in
nature. In contrast, recent work proposed rigorous approaches for computing
explanations, which hold for a given ML model and prediction over the entire
instance space. This paper extends earlier work to the case of boosted trees
and assesses the quality of explanations obtained with state-of-the-art
heuristic approaches. On most of the datasets considered, and for the vast
majority of instances, the explanations obtained with heuristic approaches are
shown to be inadequate when the entire instance space is (implicitly)
considered.
