Combining Estimates in Regression and Classification
作者:
Michael Leblanc,
Robert Tibshirani,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 436
页码: 1641-1650
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476733
出版商: Taylor & Francis Group
关键词: Bootstrap;Cross-validation;Model combination
数据来源: Taylor
摘要:
We consider the problem of how to combine a collection of general regression fit vectors to obtain a better predictive model. The individual fits may be from subset linear regression, ridge regression, or something more complex like a neural network. We develop a general framework for this problem and examine a cross-validation—based proposal called “model mix” or “stacking” in this context. We also derive combination methods based on the bootstrap and analytic methods and compare them in examples. Finally, we apply these ideas to classification problems where the estimated combination weights can yield insight into the structure of the problem.
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