在结构化数据的多个ML模型中合成可解释性
Emir Veledar1, Lili Zhou1, Omar Veledar2
1Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA.
概括
可解释机器学习 (XML) 需要可重复的方法来聚合特征的重要性. 权衡重要性得分和频率计数 (WISFC) 框架通过将不同模型的重要性大小和一致性结合起来,提供了强大的整体排名.
科学领域:
- 机器学习 机器学习
- 可解释的人工智能 (XAI)
- 数据科学数据科学数据科学
背景情况:
- 高风险领域需要在多个模型中进行可重复的特征重要性聚合.
- 现有的方法很难在解释器输出中捕捉复杂的关系.
研究的目的:
- 引入权重重要性得分和频率计数 (WISFC) 框架,以进行强大的整体特征重要性排名.
- 提供一个原则性的方法来协调和聚合不同解释者的特征重要性.
主要方法:
- 在WISFC框架聚合物中,不同解释器的输出被排名.
- 它根据模型解释器对的排名和频率分配一个加权得分.
- 这种方法将来自多个建模运行的弱信号整合起来.
主要成果:
- WISFC生成了一个强大的整体特征-重要性排名.
- 它通过聚合不同的模型视角来突出一致的重要特征.
- 该框架提供了一个比简单的共识方法更基于原则的方法.
结论:
- 通过系统地结合多个建模视角,WISFC增强了复杂系统的探索.
- 该框架可用于各种机器学习模型进行复制和概括.
- 它为研究人员和从业人员在特征重要性分析方面提供了一种新的策略.
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