{"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/deep-distribution-regression","title":"Deep Distribution Regression","arxiv_id":"1903.06023","date":"2019-03-14","proceeding":null,"authors":["Rui Li","Howard D. Bondell","Brian J. Reich"],"abstract":"Due to their flexibility and predictive performance, machine-learning based\nregression methods have become an important tool for predictive modeling and\nforecasting. However, most methods focus on estimating the conditional mean or\nspecific quantiles of the target quantity and do not provide the full\nconditional distribution, which contains uncertainty information that might be\ncrucial for decision making. In this article, we provide a general solution by\ntransforming a conditional distribution estimation problem into a constrained\nmulti-class classification problem, in which tools such as deep neural\nnetworks. We propose a novel joint binary cross-entropy loss function to\naccomplish this goal. We demonstrate its performance in various simulation\nstudies comparing to state-of-the-art competing methods. Additionally, our\nmethod shows improved accuracy in a probabilistic solar energy forecasting\nproblem.","url_abs":"http://arxiv.org/abs/1903.06023v1","url_pdf":"http://arxiv.org/pdf/1903.06023v1.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":"deep-distribution-regression","repo_url":"https://github.com/TTL30/PPE-Deep-Distribituion-Regression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}