Artificial Intelligence Colloquium - Leonardo Arrighi, PhD Student at University of Trieste

Title: Interpreting Tree-Based Ensemble Models with Decision Predicate Graphs (DPG) – A Case Study on Fruit Maturation Classification

Abstract:

The increasing adoption of tree-based ensemble models (such as Random Forests and XGBoost) in machine learning has introduced challenges in model interpretability, particularly in complex classification tasks. In this seminar, we explore Decision Predicate Graphs (DPG), a novel graph-based approach designed to enhance the interpretability of ensemble models in a global fashion. DPG provides a structured representation of decision-making processes by transforming decision paths into a directed weighted graph, preserving relationships among features and decision predicates.

Beyond theory, this session will present a real-world case study where DPG was applied to analyze a model tasked with classifying fruit maturity levels. By leveraging DPG, we uncovered how the model prioritizes features, evaluates decision constraints, and differentiates between varying degrees of ripeness. This analysis offers valuable insights into model transparency and decision logic, aiding researchers and practitioners in refining machine learning models for agricultural and food industry applications. Finally, we will discuss on DPG's future enhancements and its wider applicability in various machine learning domains.