{"id":4994,"date":"2025-07-01T07:04:14","date_gmt":"2025-07-01T07:04:14","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/01\/2506-23396\/"},"modified":"2025-07-01T07:04:14","modified_gmt":"2025-07-01T07:04:14","slug":"2506-23396","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/01\/2506-23396\/","title":{"rendered":"AICO: Feature Significance Tests for Supervised Learning"},"content":{"rendered":"

AICO: Feature Significance Tests for Supervised Learning
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arXiv:2506.23396v1 Announce Type: new
\nAbstract: 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.<\/div>\n

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\n Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
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AICO: Feature Significance Tests for Supervised Learning arXiv:2506.23396v1 Announce Type: new 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 […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[321,316,970],"class_list":["post-4994","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-feature","tag-significance","tag-tests"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4994"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=4994"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4994\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=4994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=4994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=4994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}