Research on Quantitative Evaluation and Analysis of Exercise Effect Based on Optimized XGBoost

Scientific exercise is a fundamental project to improve the comprehensive physical quality of college students. In order to effectively assess the speed and endurance quality of college students, this paper proposes an exercise effect evaluate strategy based on the XGBoost algorithm with Bayesian optimization by studying the effect of different exercise features on the exercise effect, and analyzes the important factors affecting the exercise effect by introducing the SHAP algorithm to quantify the feature contribution. Meanwhile, the differences of different kinds of motion processes are considered in motion data classification mining and evaluation to ensure the accuracy of motion effect evaluation. Finally, the performance of XGBoost based on Bayesian optimization for motion effect assessment is experimentally verified, and SHAP values are used to analyze different motion features and their importance. The algorithm proposed in this paper can not only be used to analyze the exercise effect of college students, but also provide a more advanced, reliable and accurate data analysis method for the formation of skills and breakthroughs in competitive sports, and the physical health management of public fitness.