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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| Format: | Article |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01031-0 |
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| author | R. Suguna J. Suriya Prakash H. Aditya Pai T. R. Mahesh Venkatesan Vinoth Kumar Temesgen Engida Yimer |
| author_facet | R. Suguna J. Suriya Prakash H. Aditya Pai T. R. Mahesh Venkatesan Vinoth Kumar Temesgen Engida Yimer |
| author_sort | R. Suguna |
| collection | DOAJ |
| description | 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. To analyze this impact, the research utilizes a churn dataset to evaluate how data imbalance influences model accuracy. The study utilized nine individual classifiers along with six homogeneous ensemble models to evaluate the effects of imbalanced data on model performance. Single classifier models struggle to identify underlying patterns in imbalanced data, while ensembles improve predictive performance by focusing on the minority class. However, when trained on unbalanced data, their accuracy remains subpar. The top six classifiers were selected for further investigation based on their performance on the imbalanced data. A SMOTE sampling technique was employed to create a balanced dataset, ensuring that all classes were adequately represented. The generated model’s performance improved from 61 to 79%, indicating the removal of bias in the target data. The results showed that Adaboost, an optimal classifier, demonstrated superior performance with an F1-Score of 87.6% in identifying potential churn and assessing customer account health. The findings emphasize the importance of balanced datasets for accurate ML model predictions. |
| format | Article |
| id | doaj-art-5ec2996e0b2149dcadfa3b1f956847d9 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5ec2996e0b2149dcadfa3b1f956847d92025-08-20T03:09:35ZengNature PortfolioScientific Reports2045-23222025-05-0115112010.1038/s41598-025-01031-0Mitigating class imbalance in churn prediction with ensemble methods and SMOTER. Suguna0J. Suriya Prakash1H. Aditya Pai2T. R. Mahesh3Venkatesan Vinoth Kumar4Temesgen Engida Yimer5Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyDepartment of Computer Science and Engineering, JAIN (Deemed-to-be-University)Department of CSE, MIT School of Computing, MIT Art, Design and Technology UniversityDepartment of Computer Science and Engineering, JAIN (Deemed-to-be-University)School of Computer Science Engineering and Information Systems, Vellore Institute of TechnologyDepartment of Mathematics, Dilla UniversityAbstract 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. To analyze this impact, the research utilizes a churn dataset to evaluate how data imbalance influences model accuracy. The study utilized nine individual classifiers along with six homogeneous ensemble models to evaluate the effects of imbalanced data on model performance. Single classifier models struggle to identify underlying patterns in imbalanced data, while ensembles improve predictive performance by focusing on the minority class. However, when trained on unbalanced data, their accuracy remains subpar. The top six classifiers were selected for further investigation based on their performance on the imbalanced data. A SMOTE sampling technique was employed to create a balanced dataset, ensuring that all classes were adequately represented. The generated model’s performance improved from 61 to 79%, indicating the removal of bias in the target data. The results showed that Adaboost, an optimal classifier, demonstrated superior performance with an F1-Score of 87.6% in identifying potential churn and assessing customer account health. The findings emphasize the importance of balanced datasets for accurate ML model predictions.https://doi.org/10.1038/s41598-025-01031-0Churn predictionEnsemble modelsImbalanced dataMachine learningPredictive analyticsSampling techniques |
| spellingShingle | R. Suguna J. Suriya Prakash H. Aditya Pai T. R. Mahesh Venkatesan Vinoth Kumar Temesgen Engida Yimer Mitigating class imbalance in churn prediction with ensemble methods and SMOTE Scientific Reports Churn prediction Ensemble models Imbalanced data Machine learning Predictive analytics Sampling techniques |
| title | Mitigating class imbalance in churn prediction with ensemble methods and SMOTE |
| title_full | Mitigating class imbalance in churn prediction with ensemble methods and SMOTE |
| title_fullStr | Mitigating class imbalance in churn prediction with ensemble methods and SMOTE |
| title_full_unstemmed | Mitigating class imbalance in churn prediction with ensemble methods and SMOTE |
| title_short | Mitigating class imbalance in churn prediction with ensemble methods and SMOTE |
| title_sort | mitigating class imbalance in churn prediction with ensemble methods and smote |
| topic | Churn prediction Ensemble models Imbalanced data Machine learning Predictive analytics Sampling techniques |
| url | https://doi.org/10.1038/s41598-025-01031-0 |
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