登山者:以拓驱动的视觉分析来比较本地解释
IEEE transactions on visualization and computer graphics
|June 24, 2024
概括
我们开发了Mountaineer,这是一个使用拓数据分析 (TDA) 来比较机器学习 (ML) 解释的视觉分析工具. 这有助于评估ML模型在关键应用中的透明度和问责制.
科学领域:
- 机器学习 机器学习
- 数据可视化 数据可视化
- 拓数据分析 拓数据分析
背景情况:
- 在关键应用中越来越多地使用黑盒机器学习 (ML) 模型,需要透明度和问责制.
- 现有的ML模型的局部可解释性方法由于数据的复杂性和方法的可变性而难以评估和比较.
- 拓数据分析 (TDA) 提供了一个潜在的解决方案,通过将解释转化为统一的图形表示来进行比较.
研究的目的:
- 介绍Mountaineer,一个新的以拓驱动的视觉分析工具,用于分析和比较ML解释.
- 通过将拓图形连接到数据分布,模型预测和特征归属,使ML解释的交互式探索成为可能.
- 促进对解释技术和基础数据分布的更深入的洞察,以便对模型行为做出明智的结论.
主要方法:
- 利用拓数据分析 (TDA) 将ML属性转换为可比的图形表示.
- 开发了Mountaineer,这是一个交互式视觉分析工具,用于探索这些拓图.
- 将拓图形链接回原始数据,模型预测和特征属性,以便进行全面分析.
- 通过现实数据进行案例研究和专家采访进行评估.
主要成果:
- 展示了Mountaineer比较黑子ML解释的能力,并确定方法之间分歧的原因.
- 展示了该工具在比较和理解不同ML模型中的实用性.
- 专家采访证实了该工具在获取ML解释和模型行为的洞察力方面的有效性.
结论:
- 登山者提供了一个强大的框架来评估和比较使用TDA的ML解释.
- 该工具通过促进对模型预测的更深入理解,提高了ML中的透明度和问责制.
- 登山者帮助ML从业者得出关于模型行为和解释技术的有根据的结论.
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