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计数回归模型与机器学习技术的预测性能:使用汽车保险索赔频率数据集进行比较分析
1Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa, Saudi Arabia.
PloS one
|December 31, 2024
概括
机器学习改善了汽车保险索赔频率预测,特别是零膨胀数据. 与传统方法相比,支持矢量机 (SVM) 模型显示出更高的预测准确性.
科学领域:
- 保险分析 保险分析
- 统计建模 统计建模
- 机器学习 机器学习
背景情况:
- 准确的汽车保险索赔频率预测对于风险评估和定价至关重要.
- 传统的Poisson模型在保险中常见的零膨胀数据中扎.
- 对于零膨胀保险数据集的机器学习存在有限的研究.
研究的目的:
- 研究机器学习 (ML) 模型在提高索赔频率预测准确度方面的有效性.
- 将ML模型与零膨胀汽车保险数据的传统统计模型进行比较.
- 确定在保险数据中处理零通货膨胀的最佳表现模型.
主要方法:
- 对Poisson,负二项式 (NB),零膨胀Poisson (ZIP),障碍式Poisson,零膨胀负二项式 (ZINB),障碍式负二项式,随机森林 (RF),支持向量机 (SVM) 和人工神经网络 (ANN) 模型的比较评估.
- 在性能评估中使用了平均绝对误差 (MAE).
- 将模型应用于特定的汽车保险数据集.
主要成果:
- 支持矢量机 (SVM) 显示出最高的预测准确度,有效地处理零通货膨胀.
- 零膨胀波桑 (ZIP) 和零膨胀负二项式 (ZINB) 模型也表现出强的表现.
- 与ML和零膨胀模型相比,传统的Poisson和NB模型显示出较低的预测能力.
结论:
- 机器学习,特别是SVM,为预测零膨胀汽车保险数据的索赔频率提供了显著的改进.
- 这些发现表明,保险行业需要转向先进的建模技术,以更好地管理保险业的风险.
- 建议对各种数据集进行进一步的研究,以验证这些发现.
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