{"id":4705,"date":"2025-11-12T22:47:36","date_gmt":"2025-11-12T22:47:36","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4705"},"modified":"2025-11-12T22:47:36","modified_gmt":"2025-11-12T22:47:36","slug":"confidence-calibration-for-weighted-voting-in-rf-ensembles-2","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4705","title":{"rendered":"Confidence Calibration for Weighted Voting in RF Ensembles"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"5I84srWY3z\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4700\">Open-Set Handling in RF Ensembles: Thresholding, Abstention, and OSCR Analysis<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Open-Set Handling in RF Ensembles: Thresholding, Abstention, and OSCR Analysis&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4700&#038;embed=true#?secret=RuVLUmBlCh#?secret=5I84srWY3z\" data-secret=\"5I84srWY3z\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>We investigate post-softmax calibration for weighted<br>ensemble voting in RF signal classification. Neural network confidence scores are often miscalibrated, leading to overconfident<br>predictions that degrade ensemble performance. Using per-model<br>temperature scaling, we reduce Expected Calibration Error<br>(ECE) from 15.4% to 4.2% (73% improvement) and improve<br>utility (accuracy \u00d7 coverage) from 65.6% to 71.7% (+9.3%)<br>at \u03c4 = 0.6 with &lt;0.1ms inference overhead. The approach<br>integrates directly into existing ensemble probability paths and<br>supports reproducible evaluation via synthetic or NPZ datasets.1<\/p>\n\n\n\n<p>Ensemble methods for RF signal classification combine<br>predictions from multiple neural networks to achieve superior<br>accuracy over individual models. However, modern neural networks often exhibit poor calibration\u2014their confidence scores<br>do not reflect actual prediction accuracy [1]. This miscalibration becomes particularly problematic in weighted ensemble<br>voting, where model probabilities directly influence the final<br>decision.<br>We address confidence calibration in RF ensemble classifiers through temperature scaling applied to individual model<br>logits before weighted aggregation. Our contributions include:<br>(1) systematic measurement of calibration quality using ECE<br>and MCE metrics, (2) analysis of how miscalibration affects utility under confidence-based abstention, (3) temperature<br>scaling optimization for ensemble probability paths, and (4)<br>integration hooks for production RF classification systems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We investigate post-softmax calibration for weightedensemble voting in RF signal classification. Neural network confidence scores are often miscalibrated, leading to overconfidentpredictions that degrade ensemble performance. Using per-modeltemperature scaling, we reduce Expected Calibration Error(ECE) from 15.4% to 4.2% (73% improvement) and improveutility (accuracy \u00d7 coverage) from 65.6% to 71.7% (+9.3%)at \u03c4 = 0.6 with &lt;0.1ms inference&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4705\" rel=\"bookmark\"><span class=\"screen-reader-text\">Confidence Calibration for Weighted Voting in RF Ensembles<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":2900,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[6,10],"tags":[],"class_list":["post-4705","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal-science","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4705","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4705"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4705\/revisions"}],"predecessor-version":[{"id":4710,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4705\/revisions\/4710"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/2900"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4705"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4705"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4705"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}