Bayesian Optimized Boosted Ensemble models for HR Analytics - Adopting Green Human Resource Management Practices

Employee attrition is considered a persistent and significant problem across all the leading businesses globally. This is evidenced by the fact that the issue negatively impacted not only production but also impeded the ability of businesses to maintain continuity and adopt strategic planning. Typically, employee attrition occurs when employees are dissatisfied with respective work experiences. To effectively address this issue, proactive measures can be implemented to enhance employee retention through early identification and mitigation of factors that contribute to perceived dissatisfaction in work places. In the current era of big data, people analytics has been widely adopted by human resource (HR) departments across various businesses with the aim of understanding the different workforces across distinct fields and reducing the attrition rate. As a result, organizations are presently incorporating machine learning (ML) and artificial intelligence (AI) into HR practices to help decision-makers make better, well-informed decisions about respective human resources. The application of ML has been confirmed to be the optimal method for predicting employee attrition, but the optimization of its hyperparameter can further improve the prediction accuracy. Therefore, this novel study aimed to tune the hyperparameters of boosting ML algorithm family and develop a potential tool for employee attrition prediction through the adoption of Bayesian optimization (BO). Using IBM HR Analytics dataset, the exploration compared the performance of six ensemble classifiers and identified categorical boosting (CB) as the superior model which achieved the highest accuracy of 95.8% and AUC of 0.98 with optimized hyperparameters, showing its comprehensiveness and reliability. The comparison results showed how various boosting ML variants could be used to build a promising tool that is capable of accurately predicting employee attrition and enabling HR managers to enhance employee retention as well as satisfaction.