Cross-validation set - Revision history

Vipul: /* The concept of cross-validation */

2014-07-26T02:32:42Z

The concept of cross-validation

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	The term '''cross-validation''' is used for any approach that involves dividing the learning data into a training set and a cross-validation set, possibly doing this multiple times in different ways and averaging the results of all approaches.		The term '''cross-validation''' is used for any approach that involves dividing the learning data into a training set and a cross-validation set, possibly doing this multiple times in different ways and averaging the results of all approaches.

			===Exhaustive cross-validation===

Vipul at 02:30, 26 July 2014

2014-07-26T02:30:51Z

← Older revision		Revision as of 02:30, 26 July 2014
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	The cross-validation set also differs from the [[test set]]. The test set is a subset of the labeled examples that is withheld for the entire duration of the execution of the whole machine learning problem, and is used only at the very end to judge the quality of the final result of the algorithm.		The cross-validation set also differs from the [[test set]]. The test set is a subset of the labeled examples that is withheld for the entire duration of the execution of the whole machine learning problem, and is used only at the very end to judge the quality of the final result of the algorithm.

			==The concept of cross-validation==

			The term '''cross-validation''' is used for any approach that involves dividing the learning data into a training set and a cross-validation set, possibly doing this multiple times in different ways and averaging the results of all approaches.

Vipul at 02:26, 19 June 2014

2014-06-19T02:26:27Z

← Older revision		Revision as of 02:26, 19 June 2014
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	A '''cross-validation set''' is a set of labeled examples (input-output pairs) used in [[supervised learning]] algorithms for the goal of [[hyperparameter optimization]] for the [[learning algorithm]]. It is distinguished from the [[training set]], on which we run the [[learning algorithm]] to determine the parameters. The cross-validation set may be used to tune the values of [[model hyperparameter]]s (such as the degree of the polynomial to use), [[regularization hyperparameter]]s (such as the coefficient to use for <math>L^1</math>- or <math>L^2</math>-regularization), or [[learning algorithm hyperparameter]]s (such as the [[learning rate]] or the [[number of iterations]]).		A '''cross-validation set''' is a set of labeled examples (input-output pairs) used in [[supervised learning]] algorithms for the goal of [[hyperparameter optimization]] for the [[learning algorithm]]. It is distinguished from the [[training set]], on which we run the [[learning algorithm]] to determine the parameters. The cross-validation set may be used to tune the values of [[model hyperparameter]]s (such as the degree of the polynomial to use), [[regularization hyperparameter]]s (such as the coefficient to use for <math>L^1</math>- or <math>L^2</math>-regularization), or [[learning algorithm hyperparameter]]s (such as the [[learning rate]] or the [[number of iterations]]).

	The cross-validation set also differs from the [[test set]]. ~~This~~ is a subset of the labeled examples that is withheld for the entire duration of the execution of the whole machine learning problem, and is used only at the very end to judge the quality of the final result of the algorithm.		The cross-validation set also differs from the [[test set]]. The test set is a subset of the labeled examples that is withheld for the entire duration of the execution of the whole machine learning problem, and is used only at the very end to judge the quality of the final result of the algorithm.

Vipul at 02:22, 19 June 2014

2014-06-19T02:22:44Z

← Older revision		Revision as of 02:22, 19 June 2014
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	==Definition==		==Definition==

	A '''cross-validation set''' is a set of labeled examples used in [[supervised learning]] algorithms for the goal of [[hyperparameter optimization]] for the [[learning algorithm]]. It is distinguished from the [[training set]], on which we run the [[learning algorithm]] to determine the parameters. The cross-validation set may be used to tune the values of [[model hyperparameter]]s (such as the degree of the polynomial to use), [[regularization hyperparameter]]s (such as the coefficient to use for <math>L^1</math>- or <math>L^2</math>-regularization), or [[learning algorithm hyperparameter]]s (such as the [[learning rate]] or the [[number of iterations]]).		A '''cross-validation set''' is a set of labeled examples (input-output pairs) used in [[supervised learning]] algorithms for the goal of [[hyperparameter optimization]] for the [[learning algorithm]]. It is distinguished from the [[training set]], on which we run the [[learning algorithm]] to determine the parameters. The cross-validation set may be used to tune the values of [[model hyperparameter]]s (such as the degree of the polynomial to use), [[regularization hyperparameter]]s (such as the coefficient to use for <math>L^1</math>- or <math>L^2</math>-regularization), or [[learning algorithm hyperparameter]]s (such as the [[learning rate]] or the [[number of iterations]]).

			The cross-validation set also differs from the [[test set]]. This is a subset of the labeled examples that is withheld for the entire duration of the execution of the whole machine learning problem, and is used only at the very end to judge the quality of the final result of the algorithm.

Vipul: Created page with "==Definition== A '''cross-validation set''' is a set of labeled examples used in supervised learning algorithms for the goal of hyperparameter optimization for the ..."

2014-06-19T02:21:02Z

Created page with "==Definition== A '''cross-validation set''' is a set of labeled examples used in supervised learning algorithms for the goal of hyperparameter optimization for the ..."

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==Definition==

A '''cross-validation set''' is a set of labeled examples used in [[supervised learning]] algorithms for the goal of [[hyperparameter optimization]] for the [[learning algorithm]]. It is distinguished from the [[training set]], on which we run the [[learning algorithm]] to determine the parameters. The cross-validation set may be used to tune the values of [[model hyperparameter]]s (such as the degree of the polynomial to use), [[regularization hyperparameter]]s (such as the coefficient to use for <math>L^1</math>- or <math>L^2</math>-regularization), or [[learning algorithm hyperparameter]]s (such as the [[learning rate]] or the [[number of iterations]]).