Surrogate Interpretable Graph for Random Decision Forests
This addresses the challenge of trust and regulatory compliance in health informatics by enhancing interpretability for domain experts, though it appears incremental as it builds on existing random forest methods.
The paper tackles the problem of interpreting global feature interactions in random forest models for health informatics, developing a surrogate interpretable graph method that uses graphs and mixed-integer linear programming to visualize feature usage and hierarchical interactions, improving interpretability for domain experts.
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.