用集成方法和SMOTE缓解在离职率预测中的类失衡
R Suguna1, J Suriya Prakash2, H Aditya Pai3
1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Scientific reports
|May 9, 2025
概括
不平衡的数据集显著降低了机器学习模型的准确性,特别是在流失预测中. 使用像SMOTE这样的技术来平衡数据,可以大大提高模型性能和预测可靠性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 预测分析是一种预测分析.
背景情况:
- 不平衡的数据集在机器学习中构成了重大挑战,导致有偏见的模型.
- 这种偏见特别影响预测分析应用程序,如客户流失预测.
- 准确的预测需要解决数据偏差的问题.
研究的目的:
- 调查不平衡数据集对机器学习模型准确性的影响.
- 在不平衡的数据上评估各种分类器和集合模型的性能.
- 评估数据平衡技术在提高预测性能方面的有效性.
主要方法:
- 利用流失数据集来评估数据不平衡下的模型性能.
- 对比了九个单独的分类器和六个均的整体模型.
- 应用了SMOTE (合成少数人过量采样技术) 采样方法来平衡数据集.
主要成果:
- 集成模型在不平衡的数据上显示出比单个分类器更好的预测性能,尽管准确性仍然不够理想.
- 在SMOTE平衡后,模型性能从61%提高到79%.
- 在流失预测中,Adaboost以87.6%的F1-Score取得了最高的表现.
结论:
- 数据平衡对于准确的机器学习预测至关重要.
- 在不平衡的数据集中,SMOTE有效地减轻了偏差.
- Adaboost 在流失预测任务中表现出卓越的性能.
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