在兽医教育中使用机器学习:兽医教育工作者的介绍
Sarah E Hooper1, Kent G Hecker2,3, Elpida Artemiou4
1Department of Biomedical Sciences, Ross University School of Veterinary Medicine, P.O. Box 334, Basseterre KN0101, Saint Kitts and Nevis.
Veterinary sciences
|September 27, 2023
概括
机器学习 (ML) 可以改善兽医教育,从招生到学生评估. 本指南有助于教育工作者理解和使用ML工具,包括一个实用的例子,使用Python代码来构建预测模型.
科学领域:
- 兽医医学教育 兽医医学教育
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 提供了在兽医医学教育中推进学习,教学和评估的机会.
- 关键应用包括招生流程和学生进度评估.
- 教师需要指导来理解和采用新兴的数据科学技术.
研究的目的:
- 为兽医教育工作者提供评估和采用机器学习进步的指南.
- 介绍核心的ML概念,并将其与经典统计数据进行比较.
- 为在兽医教育中应用ML提供一个实用的,逐步指南.
主要方法:
- 使用模拟的兽医学生数据来解决以假设为导向的问题.
- 使用Python语法开发一个随机森林ML预测模型.
- 展示ML模型的创建,包括处理不完整的记录.
主要成果:
- 模拟数据说明了在ML模型创建过程中教育者的决策如何影响特征的重要性.
- 在ML模型开发中的透明度对教育工作者至关重要.
- 特别强调了管理兽医教育中不完整数据的具体考虑.
结论:
- 机器学习为提高兽医医学教育提供了巨大的潜力.
- 需要进行进一步的研究,以制定处理缺失数据的指导方针和首选的模型评估方法.
- 教师在创建ML模型时必须透明,以确保可靠的洞察力.
关键词:
在这里,Python是Python.在这个过程中,R是R.人工智能的人工智能是人工智能.教育数据挖掘教育数据挖掘学习分析学习分析.机器学习是机器学习.医学教育 医学教育随机的森林随机的森林兽医教育工作者 兽医教育工作者兽医医学教育 兽医医学教育更多相关视频
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