Predictive Distribution Modeling Using Transformation Forests

Autor:	Hothorn, Torsten, Zeileis, Achim
Přispěvatelé:	University of Zurich, Hothorn, Torsten
Rok vydání:	2021
Předmět:	Statistics and Probability Computer science 610 Medicine & health Machine learning computer.software_genre Conditional expectation 01 natural sciences 010104 statistics & probability 03 medical and health sciences Simple (abstract algebra) Discrete Mathematics and Combinatorics 1804 Statistics Probability and Uncertainty 2613 Statistics and Probability 0101 mathematics 030304 developmental biology 0303 health sciences business.industry Statistics Supervised learning Regression analysis 10060 Epidemiology Biostatistics and Prevention Institute (EBPI) Conditional probability distribution Random forest Distribution (mathematics) Transformation (function) 2607 Discrete Mathematics and Combinatorics Probability and Uncertainty Artificial intelligence Statistics Probability and Uncertainty business computer
Zdroj:	Journal of Computational and Graphical Statistics. 30:1181-1196
ISSN:	1537-2715 1061-8600
Popis:	Regression models for supervised learning problems with a continuous response are commonly understood as models for the conditional mean of the response given predictors. This notion is simple and ...
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fea57353fd230d262999e0cc7491125b https://doi.org/10.1080/10618600.2021.1872581 Zobrazit plný text záznamu