Boosted regression (boosting): An introductory tutorial and a Stata plugin

Boosting, or boosted regression, is a recent data-mining technique that has shown considerable success in predictive accuracy. This article gives an overview of boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie, Tibshirani, and Friedman (2001, 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regression example, the R2 value computed on a test dataset is R2 = 21.3% for linear regression and R2 = 93.8% for boosting. In the logistic regression example, stepwise logistic regression correctly classifies 54.1% of the observations in a test dataset versus 76.0% for boosted logistic regression. Currently, boost accommodates Gaussian (normal), logistic, and Poisson boosted regression. boost is implemented as a Windows C++ plugin.


Issue Date:
2005
Publication Type:
Journal Article
DOI and Other Identifiers:
st0087 (Other)
PURL Identifier:
http://purl.umn.edu/117524
Published in:
Stata Journal, Volume 05, Number 3
Page range:
330-354
Total Pages:
25

Record appears in:



 Record created 2017-04-01, last modified 2017-04-27

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)