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

Artificial Intelligence and Machine Learning Models, Tahtalı,Y. & Bayyurt,L. & Demir,İ., Editör, Özgür Publications, Gaziantep, ss.137-157, 2026

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.

Keywords: Logistic Regression, Ridge, LASSO, Elastic Net, Human Resources

Analytics