Institute of Physical Education, Shanxi University, Taiyuan, Shanxi 030006, China.
Comput Intell Neurosci. 2022 Oct 6;2022:2039423. doi: 10.1155/2022/2039423. eCollection 2022.
The development of big data technology makes the feature selection technology gradually perfect. The advantages of different feature selection technologies are different. Among them, random forest algorithm belongs to integrated feature selection algorithm. Integrated processing of classification results can screen out the most representative feature impact. Based on this background and random forest algorithm, this paper analyzes the evaluation of motion effect. After the measurement, this paper obtains the body data before and after the training. After the calculation, the change data of the body index are determined. The random forest feature selection method is used as the carrier to determine the corresponding index attribute set. In the process of data set input, the corresponding whole input data set is formed through data classification. The completion of training, through the comparative experiment, is conducive to clear the degree of influence of physical indicators and then complete the exercise effect evaluation. The research shows that the random forest algorithm has significant advantages in the evaluation of sports effect and can effectively improve the accuracy of classification.
大数据技术的发展使得特征选择技术逐渐完善。不同特征选择技术的优势不同。其中，随机森林算法属于集成特征选择算法。通过对分类结果的综合处理，可以筛选出最具代表性的特征影响。基于此背景和随机森林算法，本文分析了运动效果的评估。测量后，本文获得了训练前后的身体数据。计算后，确定了身体指标的变化数据。随机森林特征选择方法作为载体，确定相应的指标属性集。在数据集输入过程中，通过数据分类形成相应的整体输入数据集。通过对比实验完成训练，有利于明确物理指标的影响程度，从而完成运动效果评估。研究表明，随机森林算法在运动效果评估中具有显著优势，能够有效提高分类精度。
