Hyperparameter optimization - Revision history

Vipul at 16:46, 18 January 2015

2015-01-18T16:46:25Z

← Older revision		Revision as of 16:46, 18 January 2015
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	==Definition==		==Definition==

	'''Hyperparameter optimization''' (sometimes also called '''model selection''', but not to be confused with [[model ~~type~~ selection]]) is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.		'''Hyperparameter optimization''' (sometimes also called '''model selection''', but not to be confused with [[model class selection]]) is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.

	==Typical hyperparameter choices==		==Typical hyperparameter choices==

Vipul at 18:25, 15 August 2014

2014-08-15T18:25:47Z

← Older revision		Revision as of 18:25, 15 August 2014
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	==Definition==		==Definition==

	'''Hyperparameter optimization''' (sometimes also called '''model selection''', but not to be confused with ~~the core~~ [[model selection]]) is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.		'''Hyperparameter optimization''' (sometimes also called '''model selection''', but not to be confused with [[model type selection]]) is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.

	==Typical hyperparameter choices==		==Typical hyperparameter choices==

Vipul at 16:11, 7 June 2014

2014-06-07T16:11:02Z

← Older revision		Revision as of 16:11, 7 June 2014
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	==Definition==		==Definition==

	'''Hyperparameter optimization''' is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.		'''Hyperparameter optimization''' (sometimes also called '''model selection''', but not to be confused with the core [[model selection]]) is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.

			==Typical hyperparameter choices==

			* Discrete hyperparameter such as the degree of the polynomial
			* [[Regularization hyperparameter]]: A hyperparameter that acts as a coefficient on the regularization term, used to enforce simplicity and reduce [[overfitting]]. The larger this hyperparameter, the more the enforcement of simplicity. However, very large hyperparameters may cause the model to be underfit.
			* [[Learning rate]]: This is a rate that controls how quickly the learning algorithm converges to its optimum. This isn't quite a hyperparameter that affects the choice of optimal value, but it affects the rate of convergence, and therefore it affects the value that we finally stop at.
			* Number of iterations and/or other stopping criterion parameter

Vipul: Created page with "==Definition== '''Hyperparameter optimization''' is the process of coming up with good choices of hyperparameters that control the learning algorithm used in machine..."

2014-06-07T16:02:30Z

Created page with "==Definition== '''Hyperparameter optimization''' is the process of coming up with good choices of hyperparameters that control the learning algorithm used in machine..."

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==Definition==

'''Hyperparameter optimization''' is the process of coming up with good choices of [[hyperparameter]]s that control the [[learning algorithm]] used in machine learning problems whose goal is to determine the [[parameter]]s for a functional form that predicts outputs from inputs.