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.

翻译：风险评分是一类可解释且可操作的机器学习模型，广泛应用于医学、保险和风险管理领域。与大多数计算方法不同，风险评分的设计允许人类通过基于有限标准为数据样本分配分数的方式进行计算。生成风险评分最常用的方法是使用线性回归来估计选定变量的效应。我们提出了一种构建紧凑且具有预测能力的风险评分的简单而有效的方法。我们提供了一种基于梯度提升的算法，能够建模非线性效应，并附带了C++实现以及Python和R接口。通过在涵盖回归、分类和事件时间任务的十二个表格数据集上进行广泛的实证评估，我们展示了该方法在达到具有竞争力的预测性能的同时，能够产生比基于回归的替代方案更紧凑的评分，与AutoScore相比，分类任务平均减少60%的规则，事件时间任务平均减少16%的规则。