基于方向特征相互作用的黑盒模型的解释
Aria Masoomi1, Davin Hill1, Zhonghui Xu2
1Northeastern University, Department of Electrical and Computer Engineering, Boston, MA, USA.
概括
本研究引入了一种双变量解释方法,以提高机器学习模型的透明度. 它揭示了特征相互作用,并识别了有影响力的特征,提高了模型的解释性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 可解释的人工智能
背景情况:
- 机器学习模型越来越多地使用,但它们的"黑子"性质阻碍了透明度.
- 当前的解释方法往往是单变的,重点是个别特征的重要性.
研究的目的:
- 将单变特征解释扩展到一个更高阶的双变体方法.
- 通过捕捉特征交互来增强黑盒模型的可解释性.
主要方法:
- 开发了一种代表特征相互作用作为指向图的双变量解释方法.
- 将该方法应用于Shapley值解释.
- 分析图的方向性,以识别有影响力的特征和可互换的特征组.
主要成果:
- 证明了定向解释能够揭示特征相互作用的能力.
- 展示了两种方法对最先进技术的优越性.
- 在各种数据集上验证了该方法,包括CIFAR10,IMDB,人口普查,离婚,药物和基因数据.
结论:
- 双变量解释为黑盒模型行为提供了更好的洞察力.
- 定向图分析有效地识别了特征相互作用和重要性.
- 这种方法显著提高了模型在各个领域的透明度和可解释性.
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