Surrogate Interpretable Graph for Random Decision Forests

• URL: http://arxiv.org/abs/2506.01988v1
• Date: Sat, 17 May 2025 09:44:37 GMT
• Title: Surrogate Interpretable Graph for Random Decision Forests
• Authors: Akshat Dubey, Aleksandar Anžel, Georges Hattab,
• Abstract summary: The field of health informatics has been profoundly influenced by the development of random forest models.<n>The increasing number of features and estimators in random forests can prevent domain experts from accurately interpreting global feature interactions.<n>The implementation of a surrogate interpretable graph enhances global interpretability, which is critical for such a high-stakes domain.
• Score: 45.46706627196389
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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