发表机构
University of Oklahoma（俄克拉荷马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SR4-Fit，一种内在可解释的基于规则的机器学习框架，通过稀疏松弛正则化生成紧凑稳定的规则集，在分类和回归任务上兼顾准确性与可解释性，验证于选举预测及十四个基准数据集。
AI 中文摘要
在许多高风险应用中，机器学习主要由黑盒模型主导，这些模型需要事后解释来证明其预测的合理性。然而，这些解释往往不可靠，因为它们不能反映模型的实际计算过程，从而限制了问责制和信任度。一种自然的替代方案是使用设计上即可解释的模型。然而，现有的基于规则的方法，如RuleFit和决策树，虽然透明，但往往缺乏稳定性和预测能力，这强化了传统性能指标与模型可理解性之间存在权衡的认知。为解决这一问题，我们提出了稀疏松弛正则化回归规则拟合（SR4-Fit），这是一种内在可解释的算法，适用于分类和回归任务，能够在牺牲性能的情况下生成紧凑且稳定的规则集。利用美国人口普查局美国社区调查的人口统计数据，SR4-Fit以高准确性和可解释性预测美国众议院选举结果，同时揭示了黑盒模型遗漏的人口统计交互作用。我们进一步在十四个基准数据集（六个分类和八个回归）上验证了SR4-Fit，在这些数据集上，它在准确性、稳定性和紧凑性方面优于现有的基于规则的方法（包括RuleFit和决策树），同时在预测能力上与黑盒模型保持竞争力。这些结果表明，可解释性和预测可靠性不必相互排斥，为高风险决策提供了一种实用且透明的替代方案。
英文摘要
In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions. These explanations are often unreliable because they do not reflect the model's actual computations, limiting accountability and trust. A natural alternative is to use models that are interpretable by design. However, existing rule-based approaches, such as RuleFit and decision trees, while transparent, often lack stability and predictive strength, reinforcing a perceived trade-off between traditional performance measures and model understandability. To address this, we propose Sparse Relaxed Regularized Regression Rule-Fit (SR4-Fit), an intrinsically interpretable algorithm for both classification and regression that produces compact and stable rule sets without sacrificing performance. Using demographic data from the U.S. Census Bureau's American Community Survey, SR4-Fit predicts U.S. House election outcomes with high accuracy and interpretability while uncovering demographic interactions missed by black-box models. We further validate SR4-Fit across fourteen benchmark datasets (six classification and eight regression), where it outperforms existing rule-based methods, including RuleFit and decision trees in terms of accuracy, stability, and compactness while remaining competitive with black-box models in predictivity. These results demonstrate that interpretability and predictive reliability need not be mutually exclusive, offering a practical and transparent alternative for high-stakes decision-making.
Comments77 pages, 63 figures, 23 tables
