Employee turnover prediction with machine learning: A reliable approach

Abstract

Supervised machine learning methods are described, demonstrated and assessed for the prediction of employee turnover within an organization. In this study, numerical experiments for real and simulated human resources datasets representing organizations of small-, medium-and large-sized employee populations are performed using (1) a decision tree method; (2) a random forest method; (3) a gradient boosting trees method; (4) an extreme gradient boosting method; (5) a logistic regression method; (6) support vector machines; (7) neural networks; (8) linear discriminant analysis; (9) a Naïve Bayes method; and (10) a K-nearest neighbor method. Through a robust and comprehensive evaluation process, the performance of each of these supervised machine learning methods for predicting employee turnover is analyzed and established using statistical methods. Additionally, reliable guidelines are provided on the selection, use and interpretation of these methods for the analysis of human resources datasets of varying size and complexity.