Solutions based on tree-ensemble models represent a considerable alternative to real-world prediction problems, but these models are considered black box, thus hindering their applicability in problems of sensitive contexts (such as: health and safety). Explainable Artificial Intelligence (XAI) aims to develop techniques that generate explanations of black box models, since these models are normally not self-explanatory. Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with the proposal to explain black box models through global rankings of feature relevance, which based on different methodologies, generate global explanations that indicate how the model’s inputs explain its predictions. This research aims to present an innovative XAI method, called eXirt, capable of carrying out the process of explaining tree-ensemble models, based on Item Response Theory (IRT). In this context, 41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost, Random Forest, and Gradient Boosting), and 7 XAI methods (including eXirt) were used to generate explanations. In the first set of analyses, the 164 ranks of global feature relevance generated by eXirt were compared with 984 ranks of the other XAI methods present in the literature, being verified that the new method generated different explanations from other existing methods. In a second analysis, exclusive local and global explanations generated by eXirt were presented that help in understanding the model trust, since in this explanation it is possible to observe particularities of the model regarding difficulty (if the model had difficulty predicting the test dataset), discrimination (if the model understands the test dataset as discriminative) and guesswork (if the model got the test dataset right by chance). Thus, it was verified that eXirt is able to generate global explanations of tree-ensemble models and also local and global explanations of models through IRT, showing how this consolidated theory can be used in machine learning in order to obtain explainable and reliable models.