{"id":10793,"date":"2026-02-27T07:02:29","date_gmt":"2026-02-27T07:02:29","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2026\/02\/27\/2602-22432\/"},"modified":"2026-02-27T07:02:29","modified_gmt":"2026-02-27T07:02:29","slug":"2602-22432","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2026\/02\/27\/2602-22432\/","title":{"rendered":"LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees"},"content":{"rendered":"

LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
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arXiv:2602.22432v1 Announce Type: new
\nAbstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal prediction provides distribution-free marginal coverage, yet split conformal uses a single global residual quantile and can be poorly adaptive under heteroscedasticity. Methods that improve adaptivity typically fit auxiliary nuisance models or introduce additional data splits\/partitions to learn the conformal score, increasing cost and reducing data efficiency. We propose LoBoost, a model-native local conformal method that reuses the fitted ensemble’s leaf structure to define multiscale calibration groups. Each input is encoded by its sequence of visited leaves; at resolution level k, we group points by matching prefixes of leaf indices across the first k trees and calibrate residual quantiles within each group. LoBoost requires no retraining, auxiliary models, or extra splitting beyond the standard train\/calibration split. Experiments show competitive interval quality, improved test MSE on most datasets, and large calibration speedups.<\/div>\n

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\n Vagner Santos, Victor Coscrato, Luben Cabezas, Rafael Izbicki, Thiago Ramos
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LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees arXiv:2602.22432v1 Announce Type: new Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal prediction provides distribution-free marginal coverage, yet split conformal uses a single global residual quantile and can be poorly adaptive under […]<\/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":[1020,4847,1211],"class_list":["post-10793","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-conformal","tag-loboost","tag-trees"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/10793"}],"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=10793"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/10793\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=10793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=10793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=10793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}