一种改进Deep Forest算法在保险购买预测场景中的应用研究

Application of an Improved Deep Forest Algorithm in Insurance Purchase Prediction Scenario

为了实现保险场景的精准营销,同时充分利用千万级客户和保单历史成交记录的数据特点,本文经热门算法研究和统计理论分析,提出一种基于XGBoost改造的Deep Forest级联算法。该算法采用XGBoost浅层机器学习算法作为Deep Forest级联构建块,同时用AUC-PR标准作为级联构建深度学习不平衡样本评价的自适应过程,并将此算法分别与原有XGBoost算法和原始Deep Forest算法进行性能比较。经实践,上述算法应用投产于保险购买预测场景中,分别比原有XGBoost算法和原Deep Forest算法提高5.5%和2.8%,效果显著;同时提出的浅层学习向基于Deep Forest深度优化操作流程,也为其他类似应用场景提供了实践参考方向。

In order to realize the precise marketing of the insurance scenario,and make full use of the data characteristics of tens of millions of customers and the historical transaction records of insurance policies,this paper proposes a Deep Forest cascade algorithm based on XGBoost transformation through popular algorithm research and statistical theory analysis.This algorithm adopts XGBoost shallow machine learning algorithm as the building block of Deep Forest cascade,and uses AUC-PR standard as the adaptive process of cascading deep learning unbalanced sample evaluation,and compares the performance of this algorithm with the original XGBoost algorithm and the original Deep Forest algorithm respectively.Practice has proved that the above algorithm applied in the prediction scenario of insurance purchase is improved by 5.5%and 2.8%,respectively,compared with the original XGBoost algorithm and the original Deep Forest algorithm.At the same time,the proposed shallow learning direction based on Deep Forest depth optimization operation process also provides practical reference for other similar application scenarios.