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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2018: Intelligent Systems and Applications pp 737–758Cite as

Employee Turnover Prediction with Machine Learning: A Reliable Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 869))

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Toronto, Toronto, Canada

    Yue Zhao

  2. PricewaterhouseCoopers, Toronto, Canada

    Maciej K. Hryniewicki & Francesca Cheng

  3. University of Münster, Münster, Germany

    Boyang Fu

  4. Fifth Third Bank, Cincinnati, USA

    Xiaoyu Zhu

Authors
  1. Yue Zhao
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  2. Maciej K. Hryniewicki
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  3. Francesca Cheng
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  4. Boyang Fu
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  5. Xiaoyu Zhu
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Corresponding author

Correspondence to Yue Zhao .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Zhao, Y., Hryniewicki, M.K., Cheng, F., Fu, B., Zhu, X. (2019). Employee Turnover Prediction with Machine Learning: A Reliable Approach. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_56

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