Summary of Employee Turnover Analysis Using Machine Learning Algorithms, by Mahyar Karimi et al.
Employee Turnover Analysis Using Machine Learning Algorithms
by Mahyar Karimi, Kamyar Seyedkazem Viliyani
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on developing machine learning models to predict employee turnover rates. The authors use three popular algorithms – AdaBoost, SVM, and Random Forest – to benchmark the accuracy of these models. By leveraging machine learning techniques, organizations can better monitor employee well-being features and mitigate the risks associated with high turnover rates. The study demonstrates the effectiveness of these algorithms in predicting employee attrition rates, ultimately contributing to the establishment of predictive analytics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computer programs (machine learning) to predict when employees might leave their jobs. The researchers tested three different methods to see which one works best. They want to help companies keep track of how happy and healthy their employees are, so they can stop good workers from leaving. By doing this research, the authors hope to create better tools for businesses to use. |
Keywords
* Artificial intelligence * Machine learning * Random forest
