Predictive Modeling for Employee Turnover Intention in South Korea Using CopulaGAN and Isolation Forests
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초록
This study developed a model to predict employee turnover intention using data from the 2022 Korean Labor & Income Panel Study (KLIPS) with 2471 participants. CopulaGAN and Isolation Forests were employed for data augmentation and variable importance. A logistic regression model using the augmented data achieved an accuracy of 0.80, precision of 0.60, recall of 0.72, and an F1-score of 0.65. Key variables included Job Satisfaction, Wage Satisfaction, Work Hours, Job Stability, and Job-Related Training. The study highlights the potential of these techniques for enhancing turnover prediction and aiding proactive HR strategies.
Abstract
This study developed a model to predict employee turnover intention using data from the 2022 Korean Labor & Income Panel Study (KLIPS) with 2471 participants. CopulaGAN and Isolation Forests were employed for data augmentation and variable importance. A logistic regression model using the augmented data achieved an accuracy of 0.80, precision of 0.60, recall of 0.72, and an F1-score of 0.65. Key variables included Job Satisfaction, Wage Satisfaction, Work Hours, Job Stability, and Job-Related Training. The study highlights the potential of these techniques for enhancing turnover prediction and aiding proactive HR strategies.
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