论文标题
机器学习可以发现参与保险计划的决定因素吗？比较分析
Can Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis
论文作者
论文摘要
确定影响参与的因素是成功保险计划的关键。这项研究的挑战涉及使用许多可能影响保险参与的因素来提高预测。这些相互关联的因素可以掩盖对粘附预测的影响，从而误导它们。这项研究评估了66个共同特征如何影响保险参与选择。我们依靠从2016年到2019年的FADN的单个农场数据，其中1型（野生杂货店）耕作，观察到10,926个观测。我们使用三种机器学习（ML）方法（Lasso，Boosting，Random Forest）将它们与保险模型中使用的GLM模型进行比较。 ML方法可以通过执行变量选择来有效地使用大量信息。高度准确的模型模型可以帮助我们了解影响保险参与和设计更好产品的因素。尽管保险参与问题的复杂性，ML仍然可以很好地预测。我们的结果表明，使用一组较小的回归变量，提高性能比其他两个ML工具更好。提出的ML工具确定哪些变量解释了参与选择。该信息包括选择单个变量的案例数量及其在影响参与方面的相对重要性。关注最佳解释保险参与的信息子集可以降低设计保险计划的成本。
Identifying factors that affect participation is key to a successful insurance scheme. This study's challenges involve using many factors that could affect insurance participation to make a better forecast.Huge numbers of factors affect participation, making evaluation difficult. These interrelated factors can mask the influence on adhesion predictions, making them misleading.This study evaluated how 66 common characteristics affect insurance participation choices. We relied on individual farm data from FADN from 2016 to 2019 with type 1 (Fieldcrops) farming with 10,926 observations.We use three Machine Learning (ML) approaches (LASSO, Boosting, Random Forest) compare them to the GLM model used in insurance modelling. ML methodologies can use a large set of information efficiently by performing the variable selection. A highly accurate parsimonious model helps us understand the factors affecting insurance participation and design better products.ML predicts fairly well despite the complexity of insurance participation problem. Our results suggest Boosting performs better than the other two ML tools using a smaller set of regressors. The proposed ML tools identify which variables explain participation choice. This information includes the number of cases in which single variables are selected and their relative importance in affecting participation.Focusing on the subset of information that best explains insurance participation could reduce the cost of designing insurance schemes.
