Machine learning models are widely applied in various fields. Stakeholders often use post-hoc feature importance methods to better understand the input features' contribution to the models' predictions. The interpretation of the importance values provided by these methods is frequently based on the relative order of the features (their ranking) rather than the importance values themselves. Since the order may be unstable, we present a framework for quantifying the uncertainty in global importance values. We propose a novel method for the post-hoc interpretation of feature importance values that is based on the framework and pairwise comparisons of the feature importance values. This method produces simultaneous confidence intervals for the features' ranks, which include the ``true'' (infinite sample) ranks with high probability, and enables the selection of the set of the top-k important features.

翻译：机器学习模型广泛应用于各个领域。利益相关者常采用事后特征重要性方法，以更好地理解输入特征对模型预测的贡献。这些方法提供的重要性值的解释通常基于特征的相对顺序（即排名），而非重要性值本身。由于该顺序可能不稳定，我们提出了一种量化全局重要性值不确定性的框架。基于该框架及特征重要性值的成对比较，我们提出了一种新颖的事后特征重要性解释方法。该方法可生成特征排名的同步置信区间，该区间以高概率包含“真实”（无穷样本）排名，并支持对前k个重要特征集合的选取。