对机器学习的可信度可视化重新审视:2023年现场状况
IEEE computer graphics and applications
|January 31, 2024
概括
可视化技术对于可靠的机器学习 (ML) 是至关重要的. 这项研究分析了542种技术,揭示了使用可视化来增强ML模型可解释性和信任性的日益增长的趋势.
科学领域:
- 信息可视化 信息可视化
- 视觉分析 视觉分析 视觉分析
- 机器学习 机器学习
背景情况:
- 可解释和可信赖的机器学习的可视化是一个关键的研究领域.
- 2020年之前的一份报告记录了200种技术.
- 这项研究是通过分析扩展数据集来构建该工作的.
研究的目的:
- 从2023年秋季开始,向机器学习展示可视化技术的最新发现.
- 从542种技术的扩展数据集中分析趋势和见解.
- 确定和讨论该领域的八个未解决的挑战.
主要方法:
- 系统地收集和分类关于机器学习可视化技术的同行评审文章.
- 分析了542种技术的最新数据集,使用了119个类别的图表.
- 从2023年秋季数据中检查趋势和见解.
主要成果:
- 该领域在过去三年中显示出可视化技术的快速增长趋势,以增加对机器学习模型的信任.
- 可视化有助于改进流行的模型可解释性方法.
- 视觉化在检查新的深度学习架构时是有效的.
结论:
- 可视化在推进可解释和可信的机器学习方面发挥着至关重要的作用.
- 需要继续进行研究,以应对已识别的未解决的挑战.
- 这一趋势表明,人们越来越依赖于视觉方法来开发和验证ML.
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