Enhancing employee retention : an improved hybrid model for predicting employee attrition

Abstract

Employee attrition poses a significant challenge to organizational stability, directly impacting productivity, operational continuity, and long-term strategic goals. While traditional methods of attrition management rely on retrospective analyses, machine learning (ML) offers a proactive approach to predict and mitigate workforce turnover. This study addresses the critical need for robust predictive frameworks by evaluating eight ML classifiers; Support Vector Machines with Radial Basis Function kernel, Fisher’s Linear Discriminant, Logistic Regression, Multi-Layer Perceptron, Random Forests, Naive Bayes, Adaboost and XGBoost with IBM HR Analytics Employee Attrition & Performance dataset to forecast employee attrition. A novel hybrid stacking ensemble model is proposed to enhance prediction accuracy by integrating the strengths of individual classifiers. The research investigates the impact of dataset balancing and feature optimization on model performance, emphasizing the role of data preprocessing in improving predictive reliability. Models underwent hyperparameter tuning and stratified 5-fold cross-validation, with the hybrid ensemble (Logistic Regression meta-learner) achieving superior metrics: 95.81% accuracy, 96.69% Precision, 95.77% F1-score, and 99.23% ROC-AUC. Individual models exhibited strong performance, notably XGBoost and LR (93.38% accuracy), while dataset balancing improved minority-class recall by 18–22% and feature selection reduced redundancy by 30%. Key findings reveal that dataset balancing and feature engineering significantly enhance model precision, particularly for minority class identification. The stacking model’s superior performance underscores the value of meta-learning in synthesizing heterogeneous classifier outputs. Practical implications include scalable frameworks for HR analytics, enabling early risk identification and personalized retention strategies. The stacking model’s adaptability allows integration with HR platforms, transforming reactive practices into proactive decision-making. Methodologically, the study advances predictive human capital management by demonstrating the synergy of hybrid ML approach and interpretable outputs. By bridging technical innovation with organizational psychology, this work offers a replicable blueprint for sustainable workforce management, emphasizing real-time analytics and employee-centric interventions.