Using Machine Learning to Predict and Explain Employee Attrition

Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we'll explain how we used the automated machine learning function from H2O to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy we'll also explain how we applied the new LIME package that enables breakdown of complex, black-box machine learning models into variable importance plots. Some costs are tangible such as training expenses and the time it takes from when an employee starts to when they become a productive member.