Ensemble - Revision history

Zeno Gantner at 20:22, 10 August 2011

2011-08-10T20:22:15Z

Zeno Gantner at 17:17, 6 June 2011

2011-06-06T17:17:46Z

Alan at 23:16, 1 March 2011

2011-03-01T23:16:12Z

92.231.112.169 at 10:18, 26 February 2011

2011-02-26T10:18:43Z

Alan at 12:58, 22 February 2011

2011-02-22T12:58:51Z

Zeno Gantner: new page

2011-02-17T18:07:03Z

new page

New page

'''Ensembles''' are machine learning methods that combine several models/predictors to generate more accurate predictions.
This reduces the [[variance]] of the predictions. Examples for ensemble methods are boosting, stacking, voting, bagging, and random forests, but also simple linear combinations of the predictor outputs. Ensembles are used for [[recommender systems]] to improve the quality of recommendations, and have been particularly popular in [[competitions]] like the [[Netflix Prize]], where raw prediction accuracy (as opposed to [[user satisfaction]], [[diversity]], [[serendipity]]) was used to determine the winner.

== External links ==
* [[Wikipedia: Ensemble learning]]

[[Category:Methods]]

@@ Line 1: / Line 1: @@
 '''Ensembles''' are [[machine learning]] methods that combine several models/predictors to generate more accurate predictions.
 This reduces the [[variance]] of the predictions. Examples for ensemble methods are boosting, stacking, voting, bagging, and random forests, but also simple linear combinations of the predictor outputs. Ensembles are used for [[Recommender System|recommender systems]] to improve the quality of recommendations, and have been particularly popular in [[:Category:Competition|competitions]] like the [[Netflix Prize]], where raw prediction accuracy (as opposed to [[user satisfaction]], [[diversity]], [[serendipity]]) was used to determine the winner.
 == External links ==
 * [[Wikipedia: Ensemble learning]]
-[[Category:Method]]
+[[Category: Method]]

← Older revision		Revision as of 17:17, 6 June 2011
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	* [[Wikipedia: Ensemble learning]]		* [[Wikipedia: Ensemble learning]]

−	[[Category:~~Methods~~]]	+	[[Category:Method]]

@@ Line 1: / Line 1: @@
 '''Ensembles''' are [[machine learning]] methods that combine several models/predictors to generate more accurate predictions.
-This reduces the [[variance]] of the predictions. Examples for ensemble methods are boosting, stacking, voting, bagging, and random forests, but also simple linear combinations of the predictor outputs. Ensembles are used for [[Recommender System|recommender systems]] to improve the quality of recommendations, and have been particularly popular in [[competitions]] like the [[Netflix Prize]], where raw prediction accuracy (as opposed to [[user satisfaction]], [[diversity]], [[serendipity]]) was used to determine the winner.
+This reduces the [[variance]] of the predictions. Examples for ensemble methods are boosting, stacking, voting, bagging, and random forests, but also simple linear combinations of the predictor outputs. Ensembles are used for [[Recommender System|recommender systems]] to improve the quality of recommendations, and have been particularly popular in [[:Category:Competition|competitions]] like the [[Netflix Prize]], where raw prediction accuracy (as opposed to [[user satisfaction]], [[diversity]], [[serendipity]]) was used to determine the winner.
 == External links ==

← Older revision		Revision as of 10:18, 26 February 2011
Line 1:		Line 1:
−	'''Ensembles''' are machine learning methods that combine several models/predictors to generate more accurate predictions.	+	'''Ensembles''' are [[machine learning]] methods that combine several models/predictors to generate more accurate predictions.
	This reduces the [[variance]] of the predictions. Examples for ensemble methods are boosting, stacking, voting, bagging, and random forests, but also simple linear combinations of the predictor outputs. Ensembles are used for [[Recommender System\|recommender systems]] to improve the quality of recommendations, and have been particularly popular in [[competitions]] like the [[Netflix Prize]], where raw prediction accuracy (as opposed to [[user satisfaction]], [[diversity]], [[serendipity]]) was used to determine the winner.		This reduces the [[variance]] of the predictions. Examples for ensemble methods are boosting, stacking, voting, bagging, and random forests, but also simple linear combinations of the predictor outputs. Ensembles are used for [[Recommender System\|recommender systems]] to improve the quality of recommendations, and have been particularly popular in [[competitions]] like the [[Netflix Prize]], where raw prediction accuracy (as opposed to [[user satisfaction]], [[diversity]], [[serendipity]]) was used to determine the winner.