Difference between revisions of "Bagging"
From Cohen Courses
Jump to navigationJump to searchPastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
| Line 1: | Line 1: | ||
== Summary == | == Summary == | ||
| − | Bagging (a.k.a bootstrap aggregating) is an ensemble machine learning [[category::method]] for classification and regression. | + | Bagging (a.k.a bootstrap aggregating) is an ensemble machine learning [[category::method]] for classification and regression, which generates multiple versions of a predictor and uses them to produce an aggregated predictor. |
== Method == | == Method == | ||
Revision as of 17:45, 30 November 2010
Summary
Bagging (a.k.a bootstrap aggregating) is an ensemble machine learning method for classification and regression, which generates multiple versions of a predictor and uses them to produce an aggregated predictor.
Method
...
References / Links
- Leo Brieman. Bagging Predictors. Machine Learning, 24, 123–140 (1996).
- Wikipedia article on bagging.
