Research Article
A credible predictive model for employment of college graduates based on LightGBM
@ARTICLE{10.4108/eai.17-2-2022.173456, author={Yangzi He and Jiawen Zhu and Weina Fu}, title={A credible predictive model for employment of college graduates based on LightGBM}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={6}, publisher={EAI}, journal_a={SIS}, year={2022}, month={2}, keywords={employment rate of college students, predict model classification, characteristics prediction Accuracy}, doi={10.4108/eai.17-2-2022.173456} }- Yangzi He
Jiawen Zhu
Weina Fu
Year: 2022
A credible predictive model for employment of college graduates based on LightGBM
SIS
EAI
DOI: 10.4108/eai.17-2-2022.173456
Abstract
INTRODUCTION: "Improving the employment rate of college students" directly affects the stability of the country and society and the healthy development of the industry market. The traditional graduate employment rate model only predicts the future employment rate based on changes in historical employment data in previous years. OBJECTIVES: Quantify the employment factors and solve the employment problems in colleges and universities in a targeted manner. METHODS: We construct a credible employment prediction model for college graduates based on LightGBM. RESULTS: We use the model to predict the employment status of students and obtain the special importance which is important to employment of college students. CONCLUSION: The final result shows that our Model performs well in the two indicators of accuracy and model quality.
Copyright © 2022 Yangzi He et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
