Insurance premium prediction is a problem for limited-resource farmers. Econometric methods have generated inaccurate premium forecasts. This article investigates the efficacy of machine learning in predicting insurance premium. Machine learning techniques and survey data on willingness to pay were collected from 534 farmers in Ben Tre, Khanh Hoa, Quang Ninh, and Tra Vinh Provinces, Vietnam. The top-performing models were cubist, random forest, and support vector machines. The cubist model, with the highest R² and lowest root mean square error, was the most appropriate to forecast premiums. Quantity harvested, total cost, stocking density, and willingness to participate in an insurance program were the top-ranked predictors of premium. Predicted premium payments varied by province. Partial dependence plots showed the economic relationship between predicted premium levels and selected variables. The model results demonstrate that machine learning is useful in forecasting insurance premium and exhibits promise for improving econometric techniques in premium determination.

中文翻译：

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预测越南虾保险支付意愿的机器学习方法

对于资源有限的农民来说，保险费预测是一个问题。计量经济学方法产生了不准确的保费预测。本文研究了机器学习在预测保险费方面的功效。机器学习技术和有关支付意愿的调查数据来自越南 Ben Tre、Khanh Hoa、Quang Ninh 和 Tra Vinh 省的 534 名农民。表现最好的模型是立体派、随机森林和支持向量机。立体派模型，R ^2最高和最低均方根误差，最适合预测保费。收获的数量、总成本、放养密度和参与保险计划的意愿是保费的首要预测因素。预测的保费支付因省而异。部分依赖图显示了预测保费水平和选定变量之间的经济关系。模型结果表明，机器学习可用于预测保费，并有望改善保费确定中的计量经济学技术。