用梯度提升生成更简洁精准的风险评分，可手算且效果优于传统方法。
Gradient Boosted Risk Scores
- 基于梯度提升构建非线性风险评分，支持人工计算。
- 分类任务规则数少60%，生存分析任务少16%（对比AutoScore）。
- 提供跨语言实现，适合医疗、保险等需可解释模型的场景。
风险评分是一类在医学、保险和风险管理中广泛应用的可解释且可操作的机器学习模型。与多数计算方法不同，风险评分设计为由人工根据有限指标为样本打分。现有主流方法多采用线性回归估计变量影响。本文提出一种简单有效的风险评分构建方法，基于梯度提升算法，能够建模非线性效应，并提供带Python和R绑定的C++实现。在十二个表格数据集（涵盖回归、分类和生存分析任务）上的广泛实证评估表明，该方法在预测性能上具有竞争力，同时生成的评分规则显著更紧凑：分类任务平均比AutoScore少60%规则，生存分析任务少16%。
原文摘要 · Abstract (English)
Risk scores are an interpretable and actionable class of machine learning models with applications in medicine, insurance, and risk management. Unlike most computational methods, risk scores are designed to be computed by a human by attributing points to a data sample based on a limited set of criteria. The most common approaches for generating risk scores use linear regressions to estimate the effect of selected variables. We propose a simple and effective approach towards building compact and predictive risk scores. We provide an algorithm based on gradient boosting that is capable of modeling nonlinear effects, along with a C++ implementation with Python and R bindings. Through extensive empirical evaluation on twelve tabular datasets spanning regression, classification, and time-to-event tasks, we show that our method achieves competitive predictive performance while producing substantially more compact scores than regression-based alternatives, with 60% fewer rules for classification tasks and 16% fewer rules for time-to-event tasks on average, compared to AutoScore.
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