Application of Random Forest in the analysis of students' physical health test data

Abstract

The physical fitness test data of college students is an important basis to measure the physical fitness level of college students. The application of data mining techniques to the deep mining of physical fitness data plays an important role in the physical health development of college students and the design of physical education programs. Based on the Random Forest classification algorithm, this study analyzed and explored the potential factors affecting students' physical test scores by using the physical fitness test data of the 2019 grade undergraduates of Baoji College of Arts and Sciences, and scientifically predicted the physical fitness status of college students according to the analysis results, and provided scientific guidance for physical exercise according to the analysis and prediction results. The experimental results show that the Random Forest classifier selected in this experiment has high accuracy and can provide a decision basis for the guidance of scientific physical exercise for college students.