Analysis and Prediction of Employee Turnover Characteristics based on Machine Learning

Zhang, Heng; Xu, Lexi; Cheng, Xinzhou; Chao, Kun; Zhao, Xueqing

doi:10.1109/iscit.2018.8587962

Cited by 37 publications

(9 citation statements)

References 25 publications

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“…The specific supervised MML approaches selected for the present simulation were a combination of algorithms explored by Putka et al. (2018), some of the most popular algorithms from a recent survey of data scientists engaged in competition to solve MML‐based prediction problems (Kaggle, 2020), and other popular models (Zhang et al., 2018). Each brings specific advantages and disadvantages to different prediction problems, although all are potentially appropriate in the focal scenario.…”

Section: Methodsmentioning

confidence: 99%

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A simulation of the impacts of machine learning to combine psychometric employee selection system predictors on performance prediction, adverse impact, and number of dropped predictors

Landers

Auer

Dunk

et al. 2023

Personnel Psychology

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We compare modern machine learning (MML) techniques to ordinary least squares (OLS) regression on out‐of‐sample (OOS) operational validity, adverse impact, and dropped predictor counts within a common selection scenario: the prediction of job performance from a battery of diverse psychometrically‐validated tests. In total, scores from 1.2 billion validation study participants were simulated to describe outcomes across 31,752 combinations selection system design and scoring decisions. The most consistently valuable improvement from adopting MML over traditional regression was from dropping predictors rather than by improving prediction. On average, MML improved prediction of performance from psychometric scale composites only when the ratio of sample size to scale count was less than approximately 3, although algorithm choice, predictor count, and selection ratio affected outcomes as well. We also simulated the effects of design choices when combining item scores, which showed consistent, superior predictive accuracy for several MML algorithms, especially elastic net and random forest, over traditional regression. Given these results, we suggest the potential of machine learning for employee selection is unlikely to be realized in selection systems focusing on the combination of scale composites from previously validated psychometric tests. Instead, it will be realized in unconventional design scenarios, such as the use of individual items to make multiple trait inferences, or with novel data formats like text, image, audio, video, and behavioral traces. We therefore recommend researchers focus on the potential value of MML in future selection contexts rather than continuing to focus on the current value of MML in current selection contexts.

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Section: Methodsmentioning

confidence: 99%

“…Similarly, Zhang et al (2018) predicting job performance ratings of police officers from a personality inventory, concluding that the best prediction was obtained from the use of elastic net.…”

Section: Machine Learning In Selectionmentioning

confidence: 98%

A simulation of the impacts of machine learning to combine psychometric employee selection system predictors on performance prediction, adverse impact, and number of dropped predictors

Landers

Auer

Dunk

et al. 2023

Personnel Psychology

View full text Add to dashboard Cite

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“…Zhang et al [22] have attempted to find out the most important factors that lead to employee turnover. The authors have found an essential correlation between department and work.…”

Section: Related Workmentioning

confidence: 99%

Early Prediction of Employee Turnover Using Machine Learning Algorithms

Atef

Elzanfaly

Ouf

2022

Int. j. electr. comput. eng. syst. (Online)

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Employee turnover is a serious challenge for organizations and companies. Thus, the prediction of employee turnover is a vital issue in all organizations and companies. The present work proposes prediction models for predicting the turnover intentions of workers during the recruitment process. The proposed models are based on k-nearest neighbors (KNN) and random forests (RF) machine learning algorithms. The models use the dataset of employee turnover created by IBM. The used dataset includes the most essential features, which are considered during the recruitment process of the employee and may lead to turnover. These features are salary, age, distance from home, marital status, and gender. The KNN-based model exhibited better performance in terms of accuracy, precision, F-score, specificity (SP), and false-positive rate (FPR) in comparison to the RF-based model. The models predict the average probability percentage of turnover intentions of the workers. Therefore, the models can be used to aid the human resource managers to make precautionary decisions; whether the candidate employee is likely to stay or leave the job, depending on the given relevant information about the candidate employee.

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“…The referenced model is an all-encompassing way to deal with pick the most noteworthy weighted highlights in two stages. Our primary objective is to diminish the quantity of highlights and take out the most noticeable ones from the component determination process [11].…”

Section: Introductionmentioning

confidence: 99%

Identification of human resource analytics using machine learning algorithms

2022

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Employee attrition is one of the most significant business issues in human resource (HR) analytics. This research aims to identify the most critical elements that contribute to employee attrition. Businesses operate heavily on employee training in order to maximize the returns they will offer to the company in the future. By utilizing the employee information value concept, it has been discovered that employee features such as overtime, the total number of projects and job level have a significant impact on attrition. To find the probability of new employee attrition, various classification algorithms such as decision trees (DT) classifier, logistic regression (LR), random forests (RF), and K-means clustering are used. A comparative analysis of the models with different rating scales is carried out for the highest accuracy. For prediction, four diverse machine learning (ML) algorithms such as LR, RF, DT classifier, and k-nearest neighbors (k-NN) are used. DT classifier outperforms with 97% of accuracy than other techniques. The effects of predictive ML techniques on the employee dataset show that RF evaluation outperforms other ML techniques followed by model of LR for the specific dataset if precision is the preferred metric. Identification of HR is forecasted using ML algorithms on employee data.

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Analysis and Prediction of Employee Turnover Characteristics based on Machine Learning

Cited by 37 publications

References 25 publications

A simulation of the impacts of machine learning to combine psychometric employee selection system predictors on performance prediction, adverse impact, and number of dropped predictors

A simulation of the impacts of machine learning to combine psychometric employee selection system predictors on performance prediction, adverse impact, and number of dropped predictors

Early Prediction of Employee Turnover Using Machine Learning Algorithms

Identification of human resource analytics using machine learning algorithms

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