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

Nesreen El-Rayes, Michael Smith, Stephen Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages1421-1430
Number of pages10
ISBN (Electronic)9780998133133
StatePublished - 2020
Event53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 - Maui, United States
Duration: Jan 7 2020Jan 10 2020

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2020-January
ISSN (Print)1530-1605

Conference

Conference53rd Annual Hawaii International Conference on System Sciences, HICSS 2020
Country/TerritoryUnited States
CityMaui
Period1/7/201/10/20

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Binary classification
  • Employee attrition
  • Retention strategy

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