基于SHAP值的Top-k特征搜索的有效修剪
Lisa Chabrier1,2, Anton Crombach1,2, Sergio Peignier3
1Inria, Centre de Lyon, 69603 Villeurbanne, France.
概括
TopShap有效地近似了机器学习模型中顶级特征的Shapley增量扩展 (SHAP) 值. 这种无模型算法通过智能地修剪特征搜索空间,显著减少了计算时间.
科学领域:
- 机器学习 机器学习
- 可解释的人工智能 (XAI)
背景情况:
- 机器学习模型越来越有影响力,需要方法来解释他们的预测.
- SHAP框架量化了特征对模型预测的贡献.
- 现有的SHAP方法在计算上可能很昂贵,特别是在模型不可知论方法中.
研究的目的:
- 提出TopShap,一个新型的模型不可知算法,用于高效地近似top-k SHAP值.
- 为了降低与重要特征计算SHAP值相关的计算成本.
主要方法:
- TopShap对top-k最重要的特征进行了近似的SHAP值.
- 它使用置信区间边界来动态识别和删除不能成为top-k集的一部分的特征.
- 这种修剪策略优化了计算资源分配.
主要成果:
- TopShap 展示了功能搜索空间的高效修剪.
- 与Kernel SHAP相比,执行时间的大幅减少被观察到,这是一个领先的模型不可知论方法.
- 跨多种数据集和模型的评估证实了TopShap的效率和模型不可知论.
结论:
- TopShap提供了一个计算效率高的解决方案,以模型不可知的方式近似top-k SHAP值.
- 该算法的减少执行时间的能力使其适合大规模的机器学习可解释性任务.
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