Construction and Evaluation of a Multivariate Prediction Model for Educational Expenditure in China

This research aims to construct and evaluate an innovative multivariate model for predicting China's education expenditure. In the model, we comprehensively consider several important variables such as population size, resident income, the number of educational institutions, the number of students, the number of teachers, and regional GDP. In particular, we employ the gradient boosting method and optimize it to enhance the model's predictive accuracy and generalization ability. Our model is described with detailed mathematical proofs and pseudocode algorithms to ensure its theoretical soundness and practicality. Experimental results demonstrate that, compared to traditional prediction models, our model performs better in terms of prediction accuracy and stability. The findings of this study not only provide strong technical support for the rational allocation and utilization of education funds but also offer an effective solution for similar prediction problems.