Evaluation of physical education teaching effect using Random Forest model under artificial intelligence

• DOI: 10.1016/j.heliyon.2023.e23576
• Publication Date: 2023-12-10T06:24:19Z
• Authors: Xiaowei Jiang; Yuwei Du; Yingying Zheng
• Keywords: Tree (set theory)

This work aims to optimize the physical education (PE) teaching effect based on deep learning (DL) cultivate high-level college students better. Firstly, present situation of teachers' ability is surveyed realize deficiencies in teaching. Secondly, an optimization algorithm proposed improve node splitting mode. can solve problem single and similar modes Random Forest (RF) algorithm. The independent method Iterative Dichotomiser 3 Classification Regression Tree are recombined, new rules obtained through adaptive parameter selection. Finally, scheme designed tested. results suggest: (1) During training algorithm, although loss curve at 4550 6800 points has a small crest, error network function shows downward trend tends be flat; (2) Compared with unoptimized Genetic Algorithm (GA) Algorithm-Back Propagation (GA-BP), better performance both terms time consumption accuracy (time less than 5.4 ms, more 95 %). In word, using GA-BP-RF PE feasible. model provides ideas for applying DL technology abilities.