一个机器学习模型基于凝聚力,激情和心理性来预测运动员参与度
Xin Zhang1, Zhikang Lin2, Song Gu3
1College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Scientific reports
|January 25, 2025
概括
这项研究表明,PSO-SVR机器学习模型通过分析凝聚力,激情和精神性来准确预测运动员的参与度. 这个模型提供了一个强大的工具来提高运动员的表现和理解参与因素.
科学领域:
- 运动科学 运动科学 运动科学
- 机器学习 机器学习
- 心理学 心理学 心理学
背景情况:
- 运动员的参与度对表现至关重要,并受到诸如凝聚力,激情和精神强等心理因素的影响.
- 机器学习 (ML) 提供了有效的方法来开发体育科学中的预测模型.
- 了解心理因素和运动员参与之间的相互作用对于优化表现至关重要.
研究的目的:
- 构建和比较基于ML的预测模型,以了解凝聚力,激情,精神性和运动员参与之间的内在联系.
- 确定最佳的ML模型,准确预测运动员的参与度.
- 评估各种ML算法的预测性能和可靠性.
主要方法:
- 利用机器学习算法来构建运动员参与度的预测模型.
- 研究了预测模型的构建和比较方法.
- 评估模型性能使用准确性,根平均平方误差 (RMSE),平均平方误差 (MSE) 和平均绝对误差 (MAE) 等指标.
主要成果:
- 粒子集群优化-支持向量回归 (PSO-SVR) 模型在预测运动员参与度方面表现出卓越的性能,准确度为0.9262.2.
- PSO-SVR显著优于其他先进算法,显示较低的RMSE,MSE和MAE,以及更高的R平方值.
- PSO-SVR模型的有效性归因于其强大的概括性,非线性处理能力和功能空间内的自适应优化.
结论:
- PSO-SVR模型是预测运动员参与度的高度准确和可靠的工具.
- 这项研究为影响运动员参与度的因素提供了宝贵的见解,并为改善运动员参与度提供了实际指导.
- 这些发现对体育科学研究和实践具有重大意义,可以更好地识别和优化关键参与驱动因素.
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