In this study, a prediction framework integrating random forest and multiple linear regression is constructed to focus on the quantitative analysis and prediction of multidimensional features. Firstly, a random forest decision tree is constructed based on the Gini index, and the classification and regression tasks are achieved by ranking the importance of features, completing the model parameter setting and error validation, so as to achieve the modelling and prediction of the non-linear relationship of multi-dimensional features. In addition, the study introduces independent variables such as dichotomous variables, occupancy category indicators and capacity values, fits a multiple linear regression model relying on the least squares method, and tests for multiple covariances through variance-inflated factor tests to quantify the extent to which specific factors influence the results. The framework enhances the stability and generalisation ability of multivariate system modelling through multi-level model collaboration and data processing optimisation, and provides a scalable technical paradigm for related fields.
Zhang et al. (Thu,) studied this question.