使用先进的机器学习算法来预测学术主要的完成:一个横截面研究
Alireza Kordbagheri1, Mohammadreza Kordbagheri1, Natalie Tayim2
1Department of Statistics, Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
Computers in biology and medicine
|November 12, 2024
概括
先进的机器学习模型显著提高了使用人格特征的学术主要预测,优于传统方法. 顺利,认真和情绪稳定是大学教育完成的关键预测因素.
科学领域:
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
- 教育研究教育研究
背景情况:
- 现有的从人格特征预测学术专业的方法缺乏模型复杂性和通用性.
- 这项研究通过使用先进的机器学习 (ML) 算法来解决这些差距.
研究的目的:
- 用人格子表来预测学术主要的完成.
- 评估先进的ML算法的性能与传统方法相比.
主要方法:
- 使用了59413份个人报告的数据集.
- 使用R软件,交叉验证和重新采样实现并优化了先进的ML算法 (kNN,GBE,RF).
- 使用伪R2作为一个强大的绩效指标,考虑预测的概率.
主要成果:
- 先进的ML模型在训练和测试数据集上表现出优于后勤回归的性能.
- kNN,GBE和RF模型获得了最高的分数,其中kNN获得了最高的伪R2 (0.099).
- 人格特征,特别是愉悦,认真和情绪稳定,被确定为大学教育完成的有影响力的预测因素.
结论:
- 先进的ML方法为该领域的预测任务提供了更高的准确性和有效性.
- 这些模型能够处理具有复杂模式的大型数据集,这标志着预测研究的积极未来.
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