A Comparison of Machine Learning Approaches for Predicting Employee Attrition
Source abstract
Abstract
Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI and HR Technologies · Artificial Intelligence in Healthcare · Imbalanced Data Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Informatics, Technical University of Munich, 85748 Munich, Germany
Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche (DIISM), Universitá di Siena, 53100 Siena, Italy
References (34)
Show source-provided references
1. Society for Human Resource Management (2016). Human Capital Benchmarking Report, Society for Human Resource Management. Technical Report.
2. A review of the literature on employee turnover
3. A firm-level analysis of employee attrition10.1177/1059601193184006 ↗
4. Analyzing Employee Attrition using Decision Tree Algorithms
5. Punnoose, R., and Ajit, P. (2016). Prediction of Employee Turnover in Organizations using Machine Learning Algorithms. Int. J. Adv. Res. Artif. Intell., 5.10.14569/IJARAI.2016.050904 ↗
6. Employee Attrition: What Makes an Employee Quit?
7. Fallucchi, F., Coladangelo, M., Giuliano, R., and William De Luca, E. (2020). Predicting Employee Attrition Using Machine Learning Techniques. Computers, 9.10.3390/computers9040086 ↗
8. Alduayj, S.S., and Rajpoot, K. (2018, January 18–19). Predicting Employee Attrition using Machine Learning. Proceedings of the 2018 International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates.10.1109/INNOVATIONS.2018.8605976 ↗
9. IBM (2017). IBM HR Analytics Employee Attrition & Performance, Kaggle.
10. Deep learning10.1038/nature14539 ↗
11. Bianchini, M., Dimitri, G.M., Maggini, M., and Scarselli , F. (2018). Deep neural networks for structured data. Computational Intelligence for Pattern Recognition, Springer.10.1007/978-3-319-89629-8_2 ↗
12. Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease10.1093/bib/bbaa434 ↗
13. Multimodal and multicontrast image fusion via deep generative models10.1016/j.inffus.2022.07.017 ↗
14. A geroscience approach for Parkinson’s disease: Conceptual framework and design of PROPAG-AGEING project10.1016/j.mad.2020.111426 ↗
15. Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making, Wiley.10.1002/9780470753866 ↗
16. Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.10.1002/9781118548387 ↗
17. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Routledge.
18. Random Forests10.1023/A:1010933404324 ↗
19. Rish, I. (2001, January 4–10). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA.
20. Dietterich, T.G. (2000, January 21–23). Ensemble methods in machine learning. Proceedings of the International Workshop on Multiple Classifier Systems, Cambridge, UK.10.1007/3-540-45014-9_1 ↗
21. Costa, C.J., and Aparicio, J.T. (2020, January 24–27). POST-DS: A methodology to boost data science. Proceedings of the 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), Sevilla, Spain.10.23919/CISTI49556.2020.9140932 ↗
22. Wiemer, H., Drowatzky, L., and Ihlenfeldt, S. (2019). Data mining methodology for engineering applications (DMME)—A holistic extension to the CRISP-DM model. Appl. Sci., 9.10.3390/app9122407 ↗
23. The CRISP-DM model: The new blueprint for data mining
24. Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson Correlation Coefficient. Noise Reduction in Speech Processing, Springer.10.1007/978-3-642-00296-0_5 ↗
25. An introduction to ROC analysis10.1016/j.patrec.2005.10.010 ↗
26. Patro, S.G.K., and Sahu, K.K. (2015). Normalization: A Preprocessing Stage. CoRR.10.17148/IARJSET.2015.2305 ↗
27. Analysis of explainers of black box deep neural networks for computer vision: A survey10.3390/make3040048 ↗
28. Doshi-Velez, F., and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv.
29. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead10.1038/s42256-019-0048-x ↗
30. Towards explainable deep neural networks (xDNN)10.1016/j.neunet.2020.07.010 ↗
31. The graph neural network model10.1109/TNN.2008.2005605 ↗
32. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E. (2017, January 6–11). Neural message passing for quantum chemistry. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia.
33. CaregiverMatcher: Graph Neural Networks for Connecting Caregivers of Rare Disease Patients10.1016/j.procs.2021.08.174 ↗
34. A machine learning approach for predicting hidden links in supply chain with graph neural networks10.1080/00207543.2021.1956697 ↗