{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rfcde-random-forests-for-conditional-density","title":"RFCDE: Random Forests for Conditional Density Estimation","arxiv_id":"1804.05753","date":"2018-04-16","proceeding":null,"authors":["Taylor Pospisil","Ann B. Lee"],"abstract":"Random forests is a common non-parametric regression technique which performs\nwell for mixed-type data and irrelevant covariates, while being robust to\nmonotonic variable transformations. Existing random forest implementations\ntarget regression or classification. We introduce the RFCDE package for fitting\nrandom forest models optimized for nonparametric conditional density\nestimation, including joint densities for multiple responses. This enables\nanalysis of conditional probability distributions which is useful for\npropagating uncertainty and of joint distributions that describe relationships\nbetween multiple responses and covariates. RFCDE is released under the MIT\nopen-source license and can be accessed at https://github.com/tpospisi/rfcde .\nBoth R and Python versions, which call a common C++ library, are available.","url_abs":"http://arxiv.org/abs/1804.05753v2","url_pdf":"http://arxiv.org/pdf/1804.05753v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rfcde-random-forests-for-conditional-density","repo_url":"https://github.com/tpospisi/rfcde","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"rfcde-random-forests-for-conditional-density","repo_url":"https://github.com/lee-group-cmu/RFCDE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.05753","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}