Research on Prediction Methods Based on Random Forests and Quantile Regression

Abstract

This study constructs a hybrid prediction framework that integrates random forest and quantile regression. The study first integrates multi-dimensional variables reflecting trends and attribute characteristics to construct the input feature system for the prediction model. By leveraging the ensemble learning mechanism of the random forest regression model to integrate the output results of multiple decision trees, the framework captures the distribution patterns of the data to achieve predictions for continuous variables. Additionally, the quantile regression model is introduced to select feature variables, quantify model uncertainty by setting quantiles, and generate prediction intervals. Furthermore, the study extends the application scope by incorporating a random forest classifier and conducts correlation analysis to uncover positive associations between feature variables and target variables, verifying the significant impact of multi-dimensional inputs on prediction outcomes and enhancing the model's interpretability. This framework enhances data analysis and prediction capabilities through multi-model collaboration and multi-method comprehensive analysis, demonstrating its applicability in related data processing tasks and providing a reference framework for similar studies.