Feature attributions are ubiquitous tools for understanding the predictions of machine learning models. However, popular methods for scoring input variables such as SHAP and LIME suffer from high instability due to random sampling. Leveraging ideas from multiple hypothesis testing, we devise attribution methods that correctly rank the most important features with high probability. Our algorithm RankSHAP guarantees that the $K$ highest Shapley values have the proper ordering with probability exceeding $1-\alpha$. Empirical results demonstrate its validity and impressive computational efficiency. We also build on previous work to yield similar results for LIME, ensuring the most important features are selected in the right order.

翻译：特征归因是理解机器学习模型预测的常用工具。然而，由于随机采样，SHAP和LIME等主流输入变量评分方法存在高度不稳定性。借鉴多重假设检验的思想，我们设计了能以高概率正确排序最重要特征的归因方法。我们的算法RankSHAP保证，以超过$1-\alpha$的概率，$K$个最高Shapley值具有正确的排序顺序。实验结果表明了该算法的有效性和出色的计算效率。我们还基于先前工作进行了拓展，为LIME取得了类似的结果，确保了最重要特征以正确的顺序被选出。