@article{Xiangmin Li_1, title={Supervised Learning Evaluation Model of Students’ Sports Training Efficiency Based on Random Forest Model}, url={http://forestchemicalsreview.com/index.php/JFCR/article/view/100}, DOI={10.17762/jfcr.vi.100}, abstractNote={<p>Supervised learning algorithm is widely used in industry and manufacturing. For many years,<br>the effect evaluation of sports training only stays on the basis of qualitative experience. With the<br>advent of high-tech era, sports training is a complex system engineering, which has been<br>recognized by experts all over the world. Random forest has fast operation speed and excellent<br>performance in processing big data. The existing random forest software package gives the<br>importance of all variables. Motion analysis requires more and more quantitative analysis of the<br>factors that restrict the influence. In order to accurately quantify and achieve the purpose of<br>macro-control and micro analysis, this paper expounds the algorithm of supervised learning to<br>evaluate the efficiency of students’ sports training. In view of the lack of operability of some<br>theories in the current research of sports training benefit evaluation, this paper discusses the<br>meaning of sports training benefit, the evaluation index system of competitive benefit, and the<br>lag of sports training "input" and "output". Based on the method of supervised learning, this<br>paper puts forward some ideas to solve the above problems. The experimental data show that<br>the model of evaluating the efficiency of students’ sports training based on supervised learning<br>can improve the accuracy of the evaluation of the efficiency of sports training, and provide<br>some reference for guiding students’ sports training.</p&gt;}, journal={Forest Chemicals Review}, author={Xiangmin Li}, year={1}, month={1}, pages={22-29} }