A Prediction Model for the Los Angeles Olympics Medal Table Based on a Random Forest Methodology

In order to accurately predict the medal standings of the 2028 Olympic Games in Los Angeles, this paper proposes a method of “predicting the Olympic medal standings based on the Random Forest Model.” In this paper, a total of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2}$</tex> characteristic variables were selected based on a review of the literature and an analysis of the prediction objectives. For countries that do not yet have a medal record, whether or not they can win a medal (2-categorical prediction) is used as the target variable; for countries with a history of Olympic medals, the total number of gold medals and the total number of medals are used as the target variables, and the Random Forest model is constructed separately. The empirical analysis results show that the method exhibits significant applicability characteristics, and its prediction model has good generalization ability, which can be extended and applied to the medal list prediction scenarios of multiple Olympic Games, with clear cross-cycle prediction efficacy.