Prediction Model of the Integration Effect of Equestrian Sports and Management Courses Based on Machine Learning

Based on the continuous progress of machine learning (ML) technology, its application in the field of education has attracted more and more attention. This paper focuses on the use of machine learning to build a prediction model to scientifically evaluate the effect of the integration of equestrian sports and management courses. Random Forest (RF) algorithm is adopted in the research. The algorithm improves the accuracy and generalization ability of the model to some extent by constructing multiple decision trees and integrating their prediction results. In the data processing stage, the key feature variables closely related to the course integration effect are screened out by using multi-source data collection, data cleaning and feature selection. In the process of model training, the model parameters are optimized by crossvalidation to ensure the stability and reliability of the model on different data sets. The results show that the prediction model performs well on the test data set, with an accuracy rate of $92 \%$, a recall rate of $88 \%$ and a $F 1$ score of $90 \%$, which is obviously superior to other common algorithm models. This proves the effectiveness of machine learning in the prediction of curriculum integration effect, and also provides strong support for educational decision-making.