Mitigating class imbalance in churn prediction with ensemble methods and SMOTE

Mitigating class imbalance in churn prediction with ensemble methods and SMOTE

Abstract This study examines how imbalanced datasets affect the accuracy of machine learning models, especially in predictive analytics applications such as churn prediction. When datasets are skewed towards the majority class, it can lead to biased model performance, reducing overall effectiveness....

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Bibliographic Details
Main Authors: R. Suguna, J. Suriya Prakash, H. Aditya Pai, T. R. Mahesh, Venkatesan Vinoth Kumar, Temesgen Engida Yimer
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Churn prediction
Ensemble models
Imbalanced data
Machine learning
Predictive analytics
Sampling techniques
Online Access:https://doi.org/10.1038/s41598-025-01031-0
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https://doi.org/10.1038/s41598-025-01031-0

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