{"id":4685,"date":"2025-11-12T02:30:16","date_gmt":"2025-11-12T02:30:16","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4685"},"modified":"2025-11-12T02:30:16","modified_gmt":"2025-11-12T02:30:16","slug":"confidence-calibration-for-weighted-voting-in-rf-ensembles","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4685","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=\"4gPIgYm5sq\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4681\">Confidence Calibration for Weighted Voting in RF Ensembles<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Confidence Calibration for Weighted Voting in RF Ensembles&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4681&#038;embed=true#?secret=rgWqt97PKg#?secret=4gPIgYm5sq\" data-secret=\"4gPIgYm5sq\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\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>Ensemble methods for RF signal classification combinepredictions from multiple neural networks to achieve superioraccuracy over individual models. However, modern neural networks often exhibit poor calibration\u2014their confidence scoresdo not reflect actual prediction accuracy [1]. This miscalibration becomes particularly problematic in weighted ensemblevoting, where model probabilities directly influence the finaldecision.We address confidence calibration in RF ensemble classifiers&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4685\" 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":4683,"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-4685","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\/4685","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=4685"}],"version-history":[{"count":1,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4685\/revisions"}],"predecessor-version":[{"id":4686,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4685\/revisions\/4686"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/4683"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4685"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4685"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4685"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}