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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Employee Attrition Classification Model Based on Stacking Algorithm
CHEN Yanming
LIN Xinyu
ZHAN Kunye
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DOI:10.17265/2159-5542/2023.06.006
Shantou University
South China Normal University
Shenzhen University
This paper aims to build an employee attrition classification model based on the Stacking algorithm. Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and preprocessing. Then, different algorithms are used to establish classification models as control experiments, and R-squared indicators are used to compare. Finally, the Stacking algorithm is used to establish the final classification model. This model has practical and significant implications for both human resource management and employee attrition analysis.
employee attrition, classification model, machine learning, ensemble learning, oversampling algorithm, Randomforest, stacking algorithm
Psychology Research, June 2023, Vol. 13, No. 6, 279-285
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