Analysis of physical training performance data based on decision tree
DOI:
https://doi.org/10.61173/2xvak512Keywords:
physical training, decision tree, data miningAbstract
Under the background of the current information age, the scientific, accurate and high efficiency of sports training effect evaluation is very important to improve the quality of physical exercise. However, the traditional manual evaluation method is limited by subjective and objective factors, which is often difficult to accurately reflect the training status of the students. This paper aims to solve the inaccurate and time-consuming problems in the evaluation of sports training performance, which has important practical significance and application value. Through the visualization technology to reveal the changes of the training performance, and the modeling and prediction combined with the decision tree algorithm, the accurate estimation of the training performance of the students can be realized, providing strong support for the training feedback and effect improvement.
References
[1] Chen, Y. T. (2021). Analysis and comparison of ensemble learning algorithms: Random forest and gradient boosting decision tree. Computer Knowledge and Technology, 17(15), 32- 34.
[2] Fu, W. (2021). Sports team performance prediction based on data-driven and data envelopment analysis. Journal of Shandong Sport University, 37(04), 102-111.
[3] Han, Y. H., Cao, L. J., Wang, Y. Q., & Zhang, J. J. (2022). Analysis of pest prediction model based on decision tree CART algorithm. Modernizing Agriculture, (01), 45-47.
[4] Hou, J. (2020). Prediction of university students’ academic performance based on educational data (Master’s thesis). Dalian University of Technology.
[5] Li, L. Z. (2021). Research on grade prediction model based on online learning behavior analysis (Master’s thesis). Guizhou University of Finance and Economics.
[6] Sun, J. (2021). Research on the application of Gaussian Naive Bayes algorithm in college students’ performance prediction. Computer Knowledge and Technology, 17(20), 23- 26.
[7] Sun, X. X., Zhong, H., & Chen, H. P. (2021). Statistical analysis system of student examination scores based on decision tree classification technology. Journal of Jilin University (Engineering and Technology Edition), 51(05), 1866-1872.
[8] Tang, L., & Li, F. (2021). Research on security situation prediction model of Internet of Vehicles based on decision tree. Computer Science, 48(S1), 514-517.
[9] Wang, J. Y., Zhang, Y. F., & Xu, Z. (2020). Student performance prediction based on feature selection optimization. Think Tank Times, (01), 124-125.
[10] Wang, X. X., & Tang, J. (2020). Comparative analysis of student performance prediction models. Computer Knowledge and Technology, 16(01), 199-202.
[11] She Yanda & Li Haichen. (2010). Research on credit evaluation system of credit customers based on data mining. Journal of Information, (07), 141-143+192.
[12] Xu, W., Liu, W. T., Zhan, X. Q., Xu, X. J., & Chen, X. L. (2021). Research on grade prediction method based on university classroom learning behavior. Modern Educational Technology, 31(06), 104-111.
[13] Xue, Y. N., & Yang, X. D. (2019). Application of decision tree algorithm in student performance. Science and Technology Information, 17(36), 83+85.
[14] Yao, M. H., Li, J. S., & Wang, N. (2021). Prediction of college students’ academic performance based on BP neural network. Journal of Jilin University (Information Science Edition), 39(04), 451-455.
[15] Yu, J., Bai, S. Y., & Wu, D. X. (2021). Research on online teaching student performance prediction based on machine learning. Computer Programming Skills & Maintenance, (08), 118-119+154.
[16] Zheng, A. Q., Wang, Y. Q., & Hao, C. Y. (2021). Research on online learning performance prediction based on deep learning. Computer Era, (12), 69-72+75.
[17] Zhang, J. (2021). Analysis of error theory and survey adjustment course assessment based on decision tree. Surveying and Mapping Equipment, 23(01), 32-35+59.
[18] Song, J. (2020). Research on learning behavior analysis and academic performance prediction based on campus big data (Master’s thesis).Central China Normal University. Dean&Francis ISSN 2959-6149
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
