Multi-Model Explainable AI for Employee Attrition Prediction

In this paper, I have provided a complete and explainable machine learning architecture to predict employee attrition, with a combination of standard classification models, gradient-boosting algorithms, and stacking ensemble. When experiments have been run on a large synthetic-augmented HR dataset, all methods have high success rates in prediction, with the best trade-off between accuracy, F1-macro, and ROC-AUC exhibiting by the stacking ensemble. The ROC and Precision-Recall curves also indicate that the ensemble is reliable in discrimination in the case of class imbalance, which is a requirement in the area of attrition prediction problems. In addition to the predictive ability, interpretability is identified as another essential intuition to use in the study in HR decision-making. According to the SHAP-based global elucidations, work stress, job satisfaction, work-life balance, managerial ratings, and variables related to compensation were the most significant turnover factors. To explain the individual level, LIME explanations justify particular predictions and assist the HR teams with the form of understanding the influences involved with high-risk results. Further, DiCE counterfactual analysis offers practical advice through example of how at-risk employees respond to workload, compensation, or managerial condition changes in meaningful ways to reduce the risk of attrition. In general, the findings indicate that integrating the high-performing ensemble models with the clear interpretability techniques are able to facilitate a stable and pragmatic decision-support system to workforce retention. The suggested framework is proving to be highly predictive with providing the evidence-based insights and practical recommendations to support the proactive efforts of lowering the turnover rates and preserving organizational experience.