Optimization and Analysis of Random Forest Regression Algorithm for Medal Table Prediction
Abstract
In response to the challenges of high-dimensional data overfitting, difficulty in adapting to dynamic rules, and insufficient quantification of key factors in Olympic medal prediction, this paper proposes a hybrid prediction framework integrating Graph Neural Networks, Random Forest, and dynamic causal analysis. The core innovations of the framework include: the first two-stage feature screening
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