  Understanding the decisions of tree-based ensembles and their relationships
is pivotal for machine learning model interpretation. Recent attempts to
mitigate the human-in-the-loop interpretation challenge have explored the
extraction of the decision structure underlying the model taking advantage of
graph simplification and path emphasis. However, while these efforts enhance
the visualisation experience, they may either result in a visually complex
representation or compromise the interpretability of the original ensemble
model. In addressing this challenge, especially in complex scenarios, we
introduce the Decision Predicate Graph (DPG) as a model-agnostic tool to
provide a global interpretation of the model. DPG is a graph structure that
captures the tree-based ensemble model and learned dataset details, preserving
the relations among features, logical decisions, and predictions towards
emphasising insightful points. Leveraging well-known graph theory concepts,
such as the notions of centrality and community, DPG offers additional
quantitative insights into the model, complementing visualisation techniques,
expanding the problem space descriptions, and offering diverse possibilities
for extensions. Empirical experiments demonstrate the potential of DPG in
addressing traditional benchmarks and complex classification scenarios.
