PDF

Description

Breiman[1996] showed that bagging could effectively reduce the variance of regression predictors, while leaving the bias unchanged. A new form of bagging we call adaptive bagging is effective in reducing both bias and variance. The procedure works in stages--the first stage is bagging. Based on the outcomes of the first stage, the output values are altered and a second stage of bagging is carried out using the altered output values. This is repeated until a specified noise level is reached. We give the background theory, and test the method using both trees and nearest neighbor regression methods. Application to two class classification data gives some interesting results.

Details

Title
Using adaptive bagging to debias regressions
Creator
Breiman, Leo, Author
Published
Statistics Department, University of California, Berkeley, University of California at Berkeley, Berkeley, California, Feb 1999
Full Collection Name
Statistics Technical Reports
Other Identifiers
547
Type
Text
Format
technical reports
Archive
Mathematics Statistics Library
Standard Rights Statement
Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user.
Usage Statement
Researchers may make free and open use of the UC Berkeley Library’s digitized public domain materials. However, some materials in our online collections may be protected by U.S. copyright law (Title 17, U.S.C.). Use or reproduction of materials protected by copyright beyond that allowed by fair use (Title 17, U.S.C. § 107) requires permission from the copyright owners. The use or reproduction of some materials may also be restricted by terms of University of California gift or purchase agreements, privacy and publicity rights, or trademark law. Responsibility for determining rights status and permissibility of any use or reproduction rests exclusively with the researcher. To learn more or make inquiries, please see our permissions policies (https://www.lib.berkeley.edu/about/permissions-policies).
Collection

Files

Statistics

from
to
Export
Download Full History