Work

Ensembles of decision trees and gradient-based learning for employee turnover rate prediction

Year: 2024

Type: article

Abstract: Employee turnover has a negative impact on business profitability. To tackle this issue, we can utilize computational advancements to forecast attrition and minimize expenses. We employed an HR Analytics dataset to investigate the feasibility of using these predictive models in decision support systems. We developed an ensemble of gradient-based decision trees that accurately predicted employee turnover and performed better than other sophisticated techniques. This approach demonstrates exceptional performance in handling structured and imbalanced data, effectively capturing intricate patterns. Gradient-based decision trees provide scalable solutions that effectively balance predictive accuracy and computational efficiency, making them well-suited for strategic business analysis. The importance of our findings lies in their ability to offer dependable insights for making well-informed decisions in business settings.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.2387

Authors: Chunyang Zhang, Wenjing Han

Institutions: Capital University of Economics and Business, Beijing Normal University

Language: English

Topic: AI and HR Technologies

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: gold

Estimated APC paid: $1,395