Predicting employee attrition using tree-based models

Nesreen El-Rayes, Ming Fang, Michael Smith, Stephen M. Taylor

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Purpose: The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes. Design/methodology/approach: A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition. Findings: Random forest and decision tree methods are found to be the strongest attrition prediction models. In addition, compensation, company culture and senior management performance play a primary role in an employee’s decision to leave a firm. Practical implications: This study may be used by human resources staff to better understand factors which influence employee attrition. In addition, techniques developed in this study may be applied to company-specific data sets to construct customized attrition models. Originality/value: This study contains several novel contributions which include exploratory studies such as industry job transition percentages, distributional comparisons between factors strongly contributing to employee attrition between those who left or stayed with the firm and the first comprehensive search over binary classification models to identify which provides the strongest predictive performance of employee attrition.

Original languageEnglish (US)
Pages (from-to)1273-1291
Number of pages19
JournalInternational Journal of Organizational Analysis
Issue number6
StatePublished - Oct 19 2020

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Organizational Behavior and Human Resource Management


  • Binary classification
  • Employee attrition
  • Retention strategy


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