Transparency is a non-functional requirement of machine learning that promotes interpretable or easily explainable outcomes. Unfortunately, interpretable classification models, such as linear, rule-based, and decision tree models, are superseded by more accurate but complex learning paradigms, such as deep neural networks and ensemble methods. For tabular data classification, more specifically, models based on gradient-boosted tree ensembles, such as XGBoost, are still competitive compared to deep learning ones, so they are often preferred to the latter. However, they share the same interpretability issues, due to the complexity of the learnt model and, consequently, of the predictions. While the problem of computing local explanations is largely addressed, the problem of extracting global explanations is scarcely investigated. Existing solutions consist of computing some feature importance score, or extracting approximate surrogate trees from the learnt forest, or even using a black-box explainability method. However, those methods either have poor fidelity or their comprehensibility is questionable. In this paper, we propose to fill this gap by leveraging the strong theoretical basis of the SHAP framework in the context of co-clustering and feature selection. As a result, we are able to extract shallow decision trees that explain XGBoost with competitive fidelity and higher comprehensibility compared to two recent state-of-the-art competitors.