Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

International Journal of artificial intelligence research

View Publication Info
 
 
Field Value
 
Title Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover
 
Creator Wijaya, Davin
DS, Jumri Habbeyb
Barus, Samuelta
Pasaribu, Beriman
Sirbu, Loredana Ioana
Dharma, Abdi
 
Subject Computer Science
Machine Learning; Data Mining; Uplift Modeling; Employee Turnover Prediction; Business intelligence
 
Description Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.
 
Publisher STMIK Dharma Wacana
 
Contributor Universitas Prima Indonesia
 
Date 2021-01-08
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Identifier http://ijair.id/index.php/ijair/article/view/169
10.29099/ijair.v4i2.169
 
Source International Journal of Artificial Intelligence Research; Vol 5, No 1 (2021): Articles in press
2579-7298
10.29099/ijair.v5i1
 
Language en
 
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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