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Integrating machine learning and explainable AI for employee attrition prediction in HR analytics.

Maytha Al-Ali1, Majed Alwateer2, Shatha Abed Alsaedi2

  • 1College of Business, Zayed University, Dubai, 19282, UAE.

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|February 12, 2026
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Summary

Predicting employee attrition is crucial for retention. This study introduces a machine learning framework using SHAP (SHapley Additive exPlanations) to identify key drivers like overtime and job satisfaction, enabling proactive talent management.

Keywords:
Data balancing techniquesEmployee attrition predictionFeature selectionHyperparameter optimizationJob change analysisMachine learning in HR analyticsSHAP explainability

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Area of Science:

  • Human Resources Analytics
  • Machine Learning in Business
  • Predictive Modeling for Talent Management

Background:

  • Employee attrition significantly impacts organizational productivity, morale, and financial health.
  • Effective retention strategies require accurate prediction of attrition and understanding of its root causes.
  • Current HR analytics often face challenges in predictive accuracy, interpretability, and generalizability.

Purpose of the Study:

  • To propose and evaluate a comprehensive machine learning framework for predicting employee attrition and job change likelihood.
  • To integrate advanced predictive models with explainability tools for transparent and fair HR analytics.
  • To provide actionable insights for proactive talent management and mitigate employee turnover.

Main Methods:

  • Utilized robust preprocessing pipelines and state-of-the-art machine learning models, including Adaptive Boosting (AB) and Histogram Gradient Boosting (HGB).
  • Employed SHAP (SHapley Additive exPlanations) for global and local interpretability analysis to identify key attrition predictors.
  • Addressed challenges such as class imbalance, feature selection, and model interpretability.

Main Results:

  • Achieved near-optimal performance metrics (Precision, Recall, F1-score, Accuracy) across diverse datasets.
  • Identified critical predictors of employee attrition, including OverTime, JobLevel, and JobSatisfaction, through SHAP visualizations.
  • Demonstrated the framework's adaptability, scalability, and potential for real-time deployment in HR analytics.

Conclusions:

  • The proposed machine learning framework offers a practical solution for mitigating employee turnover.
  • Enhanced predictive accuracy and interpretability in HR analytics lead to more effective talent management strategies.
  • The study advances HR analytics by bridging gaps in predicting and understanding employee attrition, safeguarding human capital investments.