Prediction of Experience Status Based on Human Resources Data: A Comparison of Lasso, Ridge, and Elastic Net Logistic Regression Models

<jats:p>This study proposes a logistic regression-based modeling approach to predict employees’ experience status using human resources data. In order to enhance model generalization and control the effects of relationships among variables, Ridge, LASSO, and Elastic Net regularization methods were examined comparatively. The models were trained on a dataset consisting of demographic and professional characteristics of employees, and their performances were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results indicate that all methods demonstrate strong classification performance and produce consistent outcomes across the dataset. Among the methods, LASSO provides more interpretable and simplified model structures, while Elastic Net offers flexibility in handling different variable structures. Ridge regression, on the other hand, contributes to model stability through coefficient shrinkage. Overall, the integration of regularization techniques with logistic regression is considered an effective approach for predictive analysis in human resources data. The study provides an analytical framework that can support decision-making processes by modeling the relationship between employee characteristics and experience status.</jats:p>