Stacked regressions

被引：43

作者：

Breiman, L

机构：

来源：

MACHINE LEARNING | 1996年 / 24卷 / 01期

关键词：

stacking; non-negativity; trees; subset regression; combinations;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Stacking regressions is a method for forming linear combinations of different predictors to give improved prediction accuracy. The idea is to use cross-validation data and least squares under non-negativity constraints to determine the coefficients in the combination. Its effectiveness is demonstrated in stacking regression trees of different sizes add in a simulation stacking linear subset and ridge regressions. Reasons why this method works are explored. The idea of stacking originated with Wolpert (1992).

引用

页码：49 / 64

页数：16

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