一种解释神经网络中个别预测的方法
1Department of Software Science, Dankook University, Youngin, Gyeonggi-do, Republic of Korea.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种方法来解释表格数据的神经网络预测,将黑子模型转化为透明工具. 该技术计算了输入值贡献,提高了对分类和回归任务的模型解释性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 高性能机器学习模型,特别是人工神经网络,经常充当"黑子",限制其预测的可解释性.
- 虽然存在可解释性方法用于图像分类,但它们在分类和回归任务中对表格数据的有效性较低.
- 在各种科学领域,越来越需要透明和可解释的预测模型.
研究的目的:
- 开发一种方法来解释来自神经网络模型的个体预测结果.
- 解决解释黑子模型的挑战,特别是表格数据.
- 为神经网络所做的预测提供明确的理由.
主要方法:
- 拟议的方法利用神经网络输出是输入的加权总和的基本原则.
- 它通过分析网络权重和输入值来计算每个输入值对最终输出的贡献.
- 贡献量化使用公式 (输入值 * 权重值) /权重总和,通过网络层跟踪影响.
主要成果:
- 开发的方法成功地解密了神经网络,使它们成为非黑子模型.
- 来自神经网络的预测得到了有效的解释,无论网络架构的复杂性 (隐藏层,节点).
- 该方法适用于分类和回归任务,并可作为一个易于使用的Python库.
结论:
- 提出的技术提高了神经网络预测对表格数据的可解释性.
- 这种方法提供了一种透明和可靠的方式来理解模型的行为.
- Python 库有助于在机器学习工作流程中实际应用这种可解释性方法.
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