Gaussian Naive Bayes - Historique des versions

Pitpitt : Page redirigée vers Classification naïve bayésienne

2022-08-01T14:31:31Z

Page redirigée vers Classification naïve bayésienne

← Version précédente		Version du 1 août 2022 à 10:31
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	~~== Définition ==~~		#REDIRECTION[[Classification naïve bayésienne]]
	~~VOIR '''~~[[~~classification~~ naïve bayésienne]]~~'''~~

	~~== Français ==~~		[[Catégorie:ENGLISH]]
	~~'''classification naïve bayésienne'''~~

	~~== Anglais ==~~
	~~'''Gaussian naïve Bayes'''~~

	~~<small>~~

	~~[https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Gaussian_na%C3%AFve_Bayes Source : Wikipedia Machine Learning ]~~


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

Pitpitt le 1 août 2022 à 13:16

2022-08-01T13:16:33Z

← Version précédente		Version du 1 août 2022 à 09:16
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	[[Catégorie:vocabulary]]		[[Catégorie:vocabulary]]
	[[Catégorie:Wikipedia-IA‎]]		[[Catégorie:Wikipedia-IA‎]]
			[[Catégorie:publication]]

ClaireGorjux le 25 novembre 2021 à 17:59

2021-11-25T17:59:07Z

← Version précédente		Version du 25 novembre 2021 à 13:59
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	~~==en construction==~~

	== Définition ==		== Définition ==
	~~XXXXXXXXX~~		VOIR '''[[classification naïve bayésienne]]'''

	== Français ==		== Français ==
	''' ~~XXXXXXXXX~~ '''		'''classification naïve bayésienne'''

	== Anglais ==		== Anglais ==
	''' Gaussian naïve Bayes'''		'''Gaussian naïve Bayes'''
	When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a normal (or Gaussian) distribution. For example, suppose the training data contains a continuous attribute, {\displaystyle x}x. We first segment the data by the class, and then compute the mean and variance of {\displaystyle x}x in each class. Let {\displaystyle \mu _{k}}\mu _{k} be the mean of the values in {\displaystyle x}x associated with class Ck, and let {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}} be the Bessel corrected variance of the values in {\displaystyle x}x associated with class Ck. Suppose we have collected some observation value {\displaystyle v}v. Then, the probability distribution of {\displaystyle v}v given a class {\displaystyle C_{k}}C_{k}, {\displaystyle p(x=v\mid C_{k})}{\displaystyle p(x=v\mid C_{k})}, can be computed by plugging {\displaystyle v}v into the equation for a normal distribution parameterized by {\displaystyle \mu _{k}}\mu _{k} and {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}}. That is,

	{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}
	Another common technique for handling continuous values is to use binning to discretize the feature values, to obtain a new set of Bernoulli-distributed features; some literature in fact suggests that this is necessary to apply naive Bayes, but it is not, and the discretization may throw away discriminative information.[5]

	Sometimes the distribution of class-conditional marginal densities is far from normal. In these cases, kernel density estimation can be used for a more realistic estimate of the marginal densities of each class. This method, which was introduced by John and Langley,[12] can boost the accuracy of the classifier considerably. [13][14]

	<small>		<small>

ClaireGorjux le 25 novembre 2021 à 17:58

2021-11-25T17:58:03Z

← Version précédente		Version du 25 novembre 2021 à 13:58
Ligne 1 :		Ligne 1 :
			==en construction==

	== Définition ==		== Définition ==
	~~VOIR '''[[classification naïve bayésienne]]'''~~		XXXXXXXXX

	== Français ==		== Français ==
	'''~~classification naïve bayésienne~~'''		''' XXXXXXXXX '''

	== Anglais ==		== Anglais ==
	'''Gaussian naïve Bayes'''		''' Gaussian naïve Bayes'''
			When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a normal (or Gaussian) distribution. For example, suppose the training data contains a continuous attribute, {\displaystyle x}x. We first segment the data by the class, and then compute the mean and variance of {\displaystyle x}x in each class. Let {\displaystyle \mu _{k}}\mu _{k} be the mean of the values in {\displaystyle x}x associated with class Ck, and let {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}} be the Bessel corrected variance of the values in {\displaystyle x}x associated with class Ck. Suppose we have collected some observation value {\displaystyle v}v. Then, the probability distribution of {\displaystyle v}v given a class {\displaystyle C_{k}}C_{k}, {\displaystyle p(x=v\mid C_{k})}{\displaystyle p(x=v\mid C_{k})}, can be computed by plugging {\displaystyle v}v into the equation for a normal distribution parameterized by {\displaystyle \mu _{k}}\mu _{k} and {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}}. That is,

			{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}
			Another common technique for handling continuous values is to use binning to discretize the feature values, to obtain a new set of Bernoulli-distributed features; some literature in fact suggests that this is necessary to apply naive Bayes, but it is not, and the discretization may throw away discriminative information.[5]

			Sometimes the distribution of class-conditional marginal densities is far from normal. In these cases, kernel density estimation can be used for a more realistic estimate of the marginal densities of each class. This method, which was introduced by John and Langley,[12] can boost the accuracy of the classifier considerably. [13][14]

	<small>		<small>

ClaireGorjux le 25 novembre 2021 à 17:57

2021-11-25T17:57:46Z

← Version précédente		Version du 25 novembre 2021 à 13:57
Ligne 1 :		Ligne 1 :
	~~==en construction==~~

	== Définition ==		== Définition ==
	~~XXXXXXXXX~~		VOIR '''[[classification naïve bayésienne]]'''

	== Français ==		== Français ==
	''' ~~XXXXXXXXX~~ '''		'''classification naïve bayésienne'''

	== Anglais ==		== Anglais ==
	''' Gaussian naïve Bayes'''		'''Gaussian naïve Bayes'''
	When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a normal (or Gaussian) distribution. For example, suppose the training data contains a continuous attribute, {\displaystyle x}x. We first segment the data by the class, and then compute the mean and variance of {\displaystyle x}x in each class. Let {\displaystyle \mu _{k}}\mu _{k} be the mean of the values in {\displaystyle x}x associated with class Ck, and let {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}} be the Bessel corrected variance of the values in {\displaystyle x}x associated with class Ck. Suppose we have collected some observation value {\displaystyle v}v. Then, the probability distribution of {\displaystyle v}v given a class {\displaystyle C_{k}}C_{k}, {\displaystyle p(x=v\mid C_{k})}{\displaystyle p(x=v\mid C_{k})}, can be computed by plugging {\displaystyle v}v into the equation for a normal distribution parameterized by {\displaystyle \mu _{k}}\mu _{k} and {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}}. That is,

	{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}
	Another common technique for handling continuous values is to use binning to discretize the feature values, to obtain a new set of Bernoulli-distributed features; some literature in fact suggests that this is necessary to apply naive Bayes, but it is not, and the discretization may throw away discriminative information.[5]

	Sometimes the distribution of class-conditional marginal densities is far from normal. In these cases, kernel density estimation can be used for a more realistic estimate of the marginal densities of each class. This method, which was introduced by John and Langley,[12] can boost the accuracy of the classifier considerably. [13][14]

	<small>		<small>

Pitpitt : Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == ''' XXXXXXXXX ''' == Anglais == ''' Gaussian naïve Bayes''' When dealing with continuous data, a typica... »

2020-12-17T17:00:48Z

Page créée avec « ==en construction== == Définition == XXXXXXXXX == Français == ''' XXXXXXXXX ''' == Anglais == ''' Gaussian naïve Bayes''' When dealing with continuous data, a typica... »

Nouvelle page

==en construction==

== Définition ==
XXXXXXXXX

== Français ==
''' XXXXXXXXX '''

== Anglais ==
''' Gaussian naïve Bayes'''
When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a normal (or Gaussian) distribution. For example, suppose the training data contains a continuous attribute, {\displaystyle x}x. We first segment the data by the class, and then compute the mean and variance of {\displaystyle x}x in each class. Let {\displaystyle \mu _{k}}\mu _{k} be the mean of the values in {\displaystyle x}x associated with class Ck, and let {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}} be the Bessel corrected variance of the values in {\displaystyle x}x associated with class Ck. Suppose we have collected some observation value {\displaystyle v}v. Then, the probability distribution of {\displaystyle v}v given a class {\displaystyle C_{k}}C_{k}, {\displaystyle p(x=v\mid C_{k})}{\displaystyle p(x=v\mid C_{k})}, can be computed by plugging {\displaystyle v}v into the equation for a normal distribution parameterized by {\displaystyle \mu _{k}}\mu _{k} and {\displaystyle \sigma _{k}^{2}}{\displaystyle \sigma _{k}^{2}}. That is,

{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}{\displaystyle p(x=v\mid C_{k})={\frac {1}{\sqrt {2\pi \sigma _{k}^{2}}}}\,e^{-{\frac {(v-\mu _{k})^{2}}{2\sigma _{k}^{2}}}}}
Another common technique for handling continuous values is to use binning to discretize the feature values, to obtain a new set of Bernoulli-distributed features; some literature in fact suggests that this is necessary to apply naive Bayes, but it is not, and the discretization may throw away discriminative information.[5]

Sometimes the distribution of class-conditional marginal densities is far from normal. In these cases, kernel density estimation can be used for a more realistic estimate of the marginal densities of each class. This method, which was introduced by John and Langley,[12] can boost the accuracy of the classifier considerably. [13][14]

<small>

[https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Gaussian_na%C3%AFve_Bayes Source : Wikipedia Machine Learning ]

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