AICO: Feature Significance Tests for Supervised Learning

• URL: http://arxiv.org/abs/2506.23396v1
• Date: Sun, 29 Jun 2025 21:15:40 GMT
• Title: AICO: Feature Significance Tests for Supervised Learning
• Authors: Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn,
• Abstract summary: This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm.<n>We construct a uniformly most powerful, randomized sign test for this median, yielding exact p-values for assessing feature significance and confidence intervals.<n>Experiments on synthetic tasks validate its statistical and computational advantages, and applications to real-world data illustrate its practical utility.
• Score: 0.5142666700569699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment -- particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm. Our method evaluates a feature's incremental contribution to model performance by masking its values across samples. Under the null hypothesis, the distribution of performance differences across a test set has a non-positive median. We construct a uniformly most powerful, randomized sign test for this median, yielding exact p-values for assessing feature significance and confidence intervals with exact coverage for estimating population-level feature importance. The approach requires minimal assumptions, avoids model retraining or auxiliary models, and remains computationally efficient even for large-scale, high-dimensional settings. Experiments on synthetic tasks validate its statistical and computational advantages, and applications to real-world data illustrate its practical utility.

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