TRUST: Transparent, Robust and Ultra-Sparse Trees
It addresses the need for accurate and interpretable models in domains requiring transparency, such as healthcare or finance, though it appears incremental by combining existing concepts like trees and LLMs.
The paper tackled the problem of interpretable regression trees lagging in predictive accuracy compared to black-box models, introducing TRUST, which matched Random Forest accuracy and outperformed other interpretable models like CART and Lasso on benchmark datasets.
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendly explanations. Extensive validation on synthetic and real-world benchmark datasets demonstrates that TRUST consistently outperforms other interpretable models -- including CART, Lasso, and Node Harvest -- in predictive accuracy, while matching the accuracy of Random Forest and offering substantial gains in both accuracy and interpretability over M5', a well-established model that is conceptually related.