EconPapers    
Economics at your fingertips  
 

Bagging, boosting and ensemble methods

Peter Bühlmann

No 2004,31, Papers from Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE)

Abstract: Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The general principleof ensemble methods is to construct a linear combinationof some model fitting methods, instead of using a single fit of the method.

Date: 2004
References: Add references at CitEc
Citations Track citations by RSS feed

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/22204/1/31_pb.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:caseps:200431

Access Statistics for this paper

More papers in Papers from Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().

 
Page updated 2018-11-24
Handle: RePEc:zbw:caseps:200431