A Study of Systematic Scale Change Based on Random Forest and Gradient Boosting Decision Trees

This study focuses on exploring the effect of key proportions in a system on overall stability using machine learning algorithms. By simulating the proportion changes and system dynamics under different conditions, the adverse effects of key proportion imbalance on system stability are revealed. In this paper, the effects of proportion changes on reproductive efficiency and overall system stability are analyzed by combining a three-level network model and a system stability index system. The relationships between proportional changes and biological and environmental indicators were analyzed in depth using random forest models and gradient boosting decision trees. The results show that these machine learning algorithms can effectively capture the important effects of proportionality changes on system indicators, which provides important insights for a deeper understanding of the role of proportionality adjustments in complex systems and helps to better predict and maintain system equilibrium.