An Explicative and Predictive Study of Employee Attrition Using Tree-based Models

Taylor, Stephen
El-Rayes, Nesreen
Smith, Michael
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We develop tree-based models to estimate the probability of an employee leaving a firm during a job transition from a dataset of anonymously submitted resumes through Glassdoor’s online portal. Dataset construction and summary statistics are first summarized followed by a more in depth examination through four exploratory studies. Insights provided by these studies are then used to engineer features that serve as input into subsequent attrition related predictive models. We finally perform a thorough search through several dozen binary classification techniques in the cases of an original and extended feature set. We find tree-based methods including random forests and light gradient boosted trees provide the overall strongest predictive performance. Finally, we summarize ROC curves for several such models and describe future potential research directions.
Machine Learning and Predictive Analytics in Accounting, Finance and Management, binary classification, employee attrition, gradient boosted trees
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