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Research on Predicting the Turnover of Graduates Using an Enhanced Random Forest Model.

Min Liu1, Bo Yang2, Yuhang Song2

  • 1School of Marxism Studies, Xi'an Polytechnic University, Xi'an 710048, China.

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Summary
This summary is machine-generated.

College graduate turnover contributes to youth unemployment. This study developed an enhanced random forest model to predict graduate turnover, identifying income and job satisfaction as key factors.

Keywords:
influencing factorsmachine learningoptimized random forest modelturnover prediction

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Area of Science:

  • Sociology
  • Management Science
  • Pedagogy

Background:

  • Frequent college graduate turnover exacerbates youth frictional and structural unemployment.
  • Limited research exists on predicting college graduate turnover, despite its significance.

Purpose of the Study:

  • To investigate college graduate turnover status in China.
  • To construct and optimize a random forest model for predicting graduate turnover.
  • To analyze the mechanisms and importance of factors influencing graduate turnover.

Main Methods:

  • Surveyed 17,268 college graduates from 52 universities in China.
  • Developed and optimized an enhanced random forest model to handle unbalanced data.
  • Analyzed individual background, job characteristics, and work environment variables.

Main Results:

  • The enhanced random forest model demonstrated high prediction accuracy and generalization ability.
  • Individual background, job characteristics, and work environment significantly influence turnover decisions.
  • Income level, job satisfaction, job opportunities, and job matching degree were the top factors impacting turnover.

Conclusions:

  • The study provides an effective model for predicting college graduate turnover.
  • Understanding key influencing factors can help mitigate graduate turnover.
  • Findings contribute to stabilizing youth employment and informing educational and management strategies.