TRUST让决策树既准又懂，还能用大模型生成易懂解释。
TRUST: Transparent, Robust and Ultra-Sparse Trees
- 融合随机森林精度与浅层树可解释性，实现高准确率
- 在真实和合成数据上超越CART、Lasso等可解释模型
- 支持大模型生成用户友好解释，适合需要透明决策的场景
分段常数回归树因其可解释性仍广受欢迎，但预测精度常落后于随机森林等黑箱模型。本文提出TRUST（透明、鲁棒、超稀疏树），一种结合随机森林精度与浅层决策树、稀疏线性模型可解释性的新型回归树模型。TRUST进一步通过大语言模型生成定制化、用户友好的解释，增强透明性。在合成与真实世界基准数据集上的大量验证表明，TRUST在预测精度上持续优于其他可解释模型（包括CART、Lasso和Node Harvest），并达到与随机森林相当的性能，同时在精度与可解释性上相较概念相关且成熟的M5'模型有显著提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。