{"id":1456,"date":"2025-01-27T07:04:54","date_gmt":"2025-01-27T07:04:54","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/27\/2501-14570\/"},"modified":"2025-01-27T07:04:54","modified_gmt":"2025-01-27T07:04:54","slug":"2501-14570","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/27\/2501-14570\/","title":{"rendered":"coverforest: Conformal Predictions with Random Forest in Python"},"content":{"rendered":"

coverforest: Conformal Predictions with Random Forest in Python
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arXiv:2501.14570v1 Announce Type: new
\nAbstract: Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial computational costs due to required pairwise comparisons between training and test samples’ out-of-bag scores. Observing that these methods naturally extend from ensemble models, particularly random forests, we leverage existing optimized random forest implementations to enable efficient cross-conformal predictions.
\n We present coverforest, a Python package that implements efficient conformal prediction methods specifically optimized for random forests. coverforest supports both regression and classification tasks through various conformal prediction methods, including split conformal, CV+, Jackknife+-after-bootstrap, and adaptive prediction sets. Our package leverages parallel computing and Cython optimizations to speed up out-of-bag calculations. Our experiments demonstrate that coverforest’s predictions achieve the desired level of coverage. In addition, its training and prediction times can be faster than an existing implementation by 2–9 times. The source code for the coverforest is hosted on GitHub at https:\/\/github.com\/donlapark\/coverforest.<\/div>\n

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\n Panisara Meehinkong, Donlapark Ponnoprat
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coverforest: Conformal Predictions with Random Forest in Python arXiv:2501.14570v1 Announce Type: new Abstract: Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,482,112],"tags":[1020,1487,121],"class_list":["post-1456","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-co","category-stat-ml","tag-conformal","tag-coverforest","tag-prediction"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1456"}],"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=1456"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1456\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1456"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1456"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1456"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}