A credible predictive model for employment of college graduates based on LightGBM

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-02-17 DOI:10.4108/eai.17-2-2022.173456
Yangzi He, Jiawen Zhu, Weina Fu
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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.
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基于LightGBM的可信大学毕业生就业预测模型
引言:“提高大学生就业率”直接关系到国家和社会的稳定以及行业市场的健康发展。传统的毕业生就业率模型只是根据往年历史就业数据的变化来预测未来的就业率。目的:量化就业因素,有针对性地解决高校就业问题。方法:基于LightGBM构建可信的大学毕业生就业预测模型。结果:运用该模型对大学生就业状况进行了预测,得到了对大学生就业具有特殊重要性的信息。结论:最终结果表明,我们的模型在准确率和模型质量两个指标上表现良好。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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