基于集体学习的电信企业客户流失的早期预警
Yancong Zhou1, Wenyue Chen1, Xiaochen Sun2
1School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
PloS one
|October 11, 2023
概括
这项研究引入了一种新的RF-Adaboost双组合模型,用于预测电信客户流失. 该模型显著提高了回忆力,精度和F1分数,优于其他机器学习方法的客户保留.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 电信分析 电信分析
背景情况:
- 客户流失给电信公司带来了巨大的运营成本.
- 预测分析可以实现有针对性的营销和个性化的服务,以减轻流失.
- 现有的机器学习模型需要对最佳的客户保留策略进行评估.
研究的目的:
- 开发和评估先进的机器学习模型,用于早期检测客户流失.
- 将集合学习模型的性能与经典机器学习算法的性能进行比较.
- 确定用于预测电信部门客户流失的最有效模型.
主要方法:
- 使用Python进行大量电信客户数据集 (900,000条记录) 的预处理,包括数据清理,过量采样和标准化.
- 实现和比较反向传播神经网络 (BPNN),随机森林 (RF) 和Adaboost模型.
- 开发了一个新的RF-Adaboost双组合模型,RF作为基础学习者.
- 使用诸如回忆率,精度率和F1分数等指标进行评估.
主要成果:
- 在所有评估指标中,RF-Adaboost双组合模型实现了最高的性能.
- 对BPNN,RF,Adaboost和RF-Adaboost的召回率分别为79%,90%,89%和93%.
- 精度率分别为97%,99%,98%和99%,F1分数分别为87%,95%,94%和96%.
- 与基准模型相比,RF-Adaboost模型的回忆率提高了10%,精度提高了1%,F1得分提高了6%.
结论:
- 该RF-Adaboost双组合模型在预测客户流失方面表现出卓越的有效性.
- 准确的流失预测为实施有效的客户保留策略提供了关键数据.
- 这种方法使电信公司能够主动减少客户 attrition 和相关成本.
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