Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
This work addresses the interpretability problem for users of machine learning models, offering an incremental improvement by enhancing visualization and analysis without fundamentally changing existing methods.
The paper tackles the challenge of interpreting tree-based ensemble models by introducing Decision Predicate Graphs (DPG), a model-agnostic tool that provides global interpretation and quantitative insights, demonstrating its potential in benchmarks and complex classification scenarios.
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.