Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost

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dc.contributor.authorAbere, Reuben Akporube et. al.
dc.date.accessioned2026-02-13T11:32:51Z
dc.date.available2026-02-13T11:32:51Z
dc.date.issued2024
dc.description.abstractCustomer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.
dc.identifier.citationAbere, R. A. et. al. (2024) Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost; Journal of Computing Theories and Applications, vol. 2, no. 1, pp 86 - 101. https://doi.org/10.62411/jcta.10562
dc.identifier.issn3024-9104
dc.identifier.urihttps://repository.fupre.edu.ng/handle/123456789/163
dc.language.isoen
dc.publisherJournal of Computing Theories and Applications
dc.titleEffects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost
dc.typeArticle
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