Cross-validation set: Difference between revisions
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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. | |||
Revision as of 02:22, 19 June 2014
Definition
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 hyperparameters (such as the degree of the polynomial to use), regularization hyperparameters (such as the coefficient to use for - or -regularization), or learning algorithm hyperparameters (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.