International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 3
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Evaluating Explainability Tradeoffs in Machine Learning-Based Retention Intelligence Systems

Author(s) Wael Breich
Country United States
Abstract Machine learning systems are increasingly used in customer churn analytics, yet many high-performing predictive models suffer from limited interpretability. This study investigatesexplainability trade-offs in churn prediction using a telecommunications dataset containingapproximately 3,150 customer records and a combination of behavioral, demographic, billing,and subscription-related attributes. Logistic regression, random forest, and XGBoost modelswere comparatively evaluated to analyze the relationship between predictive performance andinterpretability.To improve transparency within the nonlinear ensemble framework, SHapley AdditivexPlanations (SHAP) were applied to the XGBoost model to generate both global and localbehavioral explanations. The results show that XGBoost achieved the strongest predictiveperformance, while SHAP substantially improved interpretability by identifying the customer
attributes most strongly associated with churn predictions. Engagement and subscription-relatedvariables emerged as the most influential behavioral drivers.
The findings demonstrate that explainable artificial intelligence can improve the operationaltransparency and usability of high-performing churn prediction systems without substantiallysacrificing predictive capability.
Keywords Explainable Artificial Intelligence, SHAP, Machine Learning Interpretability, Customer Churn Analytics, XGBoost, Behavioral Analytics
Field Computer > Data / Information
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-22
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.79010
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E-ISSN 2582-2160
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