Surrogate Interpretable Graph for Random Decision Forests

TL;DR

This paper introduces a surrogate interpretability graph method that visualizes feature interactions in random decision forests, improving global interpretability crucial for health informatics applications.

Contribution

It proposes a novel graph-based approach using mixed-integer linear programming to enhance interpretability of complex random forest models.

Findings

Improved visualization of feature interactions

Enhanced global interpretability of random forests

Facilitated trust and regulatory compliance

Abstract

The field of health informatics has been profoundly influenced by the development of random forest models, which have led to significant advances in the interpretability of feature interactions. These models are characterized by their robustness to overfitting and parallelization, making them particularly useful in this domain. However, the increasing number of features and estimators in random forests can prevent domain experts from accurately interpreting global feature interactions, thereby compromising trust and regulatory compliance. A method called the surrogate interpretability graph has been developed to address this issue. It uses graphs and mixed-integer linear programming to analyze and visualize feature interactions. This improves their interpretability by visualizing the feature usage per decision-feature-interaction table and the most dominant hierarchical decision feature interactions for predictions. The implementation of a surrogate interpretable graph enhances global interpretability, which is critical for such a high-stakes domain.