基于混合组合-融合模型的客户流失预测的新型分类算法
Chenggang He1,2, Chris H Q Ding3,4
1School of Public Safety and Emergency Management, Anhui University of Science and Technology, No.15 Fengxia Road, Hefei, 230041, Anhui, China. hechenggang@aust.edu.cn.
Scientific reports
|August 30, 2024
概括
预测客户流失率对于企业健康至关重要. 一个Ensemble-Fusion机器学习模型实现了95.35%的准确性,超过了其他17个算法来减少客户 attrition.
科学领域:
- 机器学习 机器学习
- 业务分析 业务分析
- 预测建模预测建模
背景情况:
- 客户流失严重影响业务健康和收入.
- 准确预测客户消耗是一个持续的行业挑战.
- 评估用于流失预测的机器学习算法需要全面的比较.
研究的目的:
- 开发和评估用于预测客户流失的先进机器学习模型.
- 引入一个智能系统,利用集体融合模型来缓解客户消耗.
- 为了比较Ensemble-Fusion模型与17个已建立的机器学习算法的性能.
主要方法:
- 实现集成融合模型用于客户流失预测.
- 涉及9个主要类别的17个机器学习算法的比较分析.
- 评估指标包括准确性,曲线下面积 (AUC) 和F1分数.
主要成果:
- 组合-融合模型实现了95.35%的数据预测准确度.
- 该模型的AUC得分为91%,F1得分为96.96%.
- 与基准算法相比,Ensemble-Fusion模型表现出优越的性能.
结论:
- 集体融合模型在预测客户流失方面非常有效.
- 这种智能系统为减少客户消耗提供了一个有希望的解决方案.
- 这些发现支持在商业战略中采用先进的机器学习技术.
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