论文标题
模型比较和校准评估：机器学习和精算实践中一致评分功能的用户指南
Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice
论文作者
论文摘要
精算师和数据科学家的主要任务之一是为某些现象（例如索赔规模或保险中的索赔数量）建立良好的预测模型。这些模型理想地利用给定的特征信息以提高预测的准确性。该用户指南重新审视并阐明了统计技术，以一方面评估模型的校准或充分性，另一方面比较和对不同的模型进行比较和排名。在此过程中，它强调了指定先验的预测目标功能的重要性（例如，平均值或分位数），并选择与该目标功能一致的模型比较中选择评分函数。提供了评分功能的实际选择指南。为了弥合科学与日常实践之间的差距，它主要关注现有结果和最佳实践的教学表现。结果伴随并说明了两项有关工人补偿和客户流失的实际数据案例研究。
One of the main tasks of actuaries and data scientists is to build good predictive models for certain phenomena such as the claim size or the number of claims in insurance. These models ideally exploit given feature information to enhance the accuracy of prediction. This user guide revisits and clarifies statistical techniques to assess the calibration or adequacy of a model on the one hand, and to compare and rank different models on the other hand. In doing so, it emphasises the importance of specifying the prediction target functional at hand a priori (e.g. the mean or a quantile) and of choosing the scoring function in model comparison in line with this target functional. Guidance for the practical choice of the scoring function is provided. Striving to bridge the gap between science and daily practice in application, it focuses mainly on the pedagogical presentation of existing results and of best practice. The results are accompanied and illustrated by two real data case studies on workers' compensation and customer churn.
