Bias-variance dilemma - Historique des versions

Pitpitt : Page redirigée vers Compromis biais-variance

2021-12-09T16:35:30Z

Page redirigée vers Compromis biais-variance

← Version précédente		Version du 9 décembre 2021 à 12:35
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	~~==en construction==~~		#REDIRECTION[[Compromis biais-variance]]
	[~~doublon~~]

	~~== Définition ==~~		[[Catégorie:ENGLISH]]
	~~XXXXXXXXX~~

	~~== Français ==~~
	~~''' XXXXXXXXX '''~~

	~~== Anglais ==~~
	~~''' Bias-variance dilemma'''~~

	~~''' Bias-variance tradeoff'''~~

	In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:[1][2]

	~~The bias error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).~~
	The variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting).

	~~<small>~~

	~~[https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Source : Wikipedia Machine Learning ]~~

	~~[https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html Source : kdnuggets]~~

	[[Catégorie:~~vocabulary~~]]

Pitpitt le 18 octobre 2021 à 15:48

2021-10-18T15:48:33Z

← Version précédente		Version du 18 octobre 2021 à 11:48
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	[https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Source : Wikipedia Machine Learning ]		[https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Source : Wikipedia Machine Learning ]

	https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html		[https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html Source : kdnuggets]

	[[Catégorie:vocabulary]]		[[Catégorie:vocabulary]]
	~~[[Catégorie:Wikipedia-IA‎]]~~

Isaline le 12 mars 2021 à 16:42

2021-03-12T16:42:29Z

← Version précédente		Version du 12 mars 2021 à 12:42
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	==en construction==		==en construction==
			[doublon]

	== Définition ==		== Définition ==

Pitpitt le 7 janvier 2021 à 03:52

2021-01-07T03:52:32Z

← Version précédente		Version du 6 janvier 2021 à 23:52
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	== Anglais ==		== Anglais ==
	''' Bias-variance dilemma'''		''' Bias-variance dilemma'''

			''' Bias-variance tradeoff'''

	In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:[1][2]		In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:[1][2]
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	[https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Source : Wikipedia Machine Learning ]		[https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Source : Wikipedia Machine Learning ]

			https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html

	[[Catégorie:vocabulary]]		[[Catégorie:vocabulary]]
	[[Catégorie:Wikipedia-IA‎]]		[[Catégorie:Wikipedia-IA‎]]

Pitpitt : Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == ''' XXXXXXXXX ''' == Anglais == ''' Bias-variance dilemma''' In statistics and machine learning, the bi... »

2020-12-18T02:19:15Z

Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == ''' XXXXXXXXX ''' == Anglais == ''' Bias-variance dilemma''' In statistics and machine learning, the bi... »

Nouvelle page

==en construction==

== Définition ==
XXXXXXXXX

== Français ==
''' XXXXXXXXX '''

== Anglais ==
''' Bias-variance dilemma'''

In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:[1][2]

The bias error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).
The variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting).

<small>

[https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff Source : Wikipedia Machine Learning ]

[[Catégorie:vocabulary]]
[[Catégorie:Wikipedia-IA‎]]