Explaining a classification made by tree-ensembles is an inherently hard problem that is traditionally solved approximately, without guaranteeing sufficiency or necessity. Abductive explanations were the first attempt to provide concise sufficient information: Given a sample, they consist of the minimal set of features that are relevant for the outcome. Inflated explanations are a refinement that additionally specify how much at least one feature must be altered in order to allow a change of the prediction. In this paper, we present the first algorithm for generating inflated explanations for gradient boosted trees, today’s de facto standard for tree-based classifiers. Key to our algorithm is a compilation approach based on algebraic decision diagrams. The impact of our approach is illustrated along a number of popular data sets.