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Classificateur Bayes naïf
Naive Bayes is a probabilistic classifier based on Bayes’ theorem with a conditional-independence assumption for features given a class.
Déglul tegtal biiIA-nettali · ~1 min
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Résumé
Its variants use different likelihood models for different feature types.
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Naive Bayes applies Bayes’ theorem to estimate a class posterior from a prior and feature likelihoods. Its simplifying assumption treats features as conditionally independent given the class. Multinomial and Bernoulli variants use count or binary feature representations; Gaussian Naive Bayes estimates a mean and variance for each feature within each class. These models differ in their likelihood assumptions and should be selected to match the data representation.
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Multinomial Naive Bayes models discrete counts such as word counts.
Bernoulli Naive Bayes models binary feature presence or absence.
Gaussian Naive Bayes models continuous features with class-conditional normal distributions.
A lightweight baseline can be compared with other classifiers on held-out task data.
Ëlëgu Classificateurs Bayes Naïfs
Use Naive Bayes as a lightweight baseline when its assumptions and computational profile fit the problem. Compare it with alternatives on held-out data; performance depends on the dataset, features and metric.
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Moytul
- Ekip yu bari mën nañu jëfandikoo benn baat ci anam wu wuute, kon teela leeral yaatuwaayam.
- Benchmark yi mën nañu nuru lu am doole waaye performance yi ci àdduna bi duñu tolloo.
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- Tàmbaleel ci joxe leeral ci làkk wu leer ci njariñ li nga soxla.
- Tannal benn metric bu baax ak benn anam bu baaxul balaa ngay saytu.
- Doxal ab pilote bu ndaw ak ay done yu representatif, du ab demo bu leer.
- Këyit fi Naïf Bayes Classifiers di jàppale ak fi pexe yu gëna yomba gëna baax.
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Take the Naive Bayes Classifiers quiz
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Luy Classificateur Bayes Naïf?
Naive Bayes is a probabilistic classifier based on Bayes’ theorem with a conditional-independence assumption for features given a class. Its variants use different likelihood models for different feature types.
Lan mooy 'naïf' bi gëna am solo ci benn klassifikatëru Bayes bu Naïf?
Modèle bi dafay xalaat ni man-man bu nekk dafay jàppale boppam ci resultaa biñ jox klaas bi, suko defee mu yokk probabilite yi ci man-man bu nekk.
Ban theorem la Naïf Bayes tabax?
Dafay jëfandikoo theorem bu Bayes ngir soppi probabilite yi ci klas bu njëkk bi ak probabilite yi ci màndarga yi ci probabilite yi ci ginaaw ngir klas bu nekk.
Lan moo waral jëfandikoo gi dafay faral di boole ay probabilite log ci barabu yokk probabilite yu bees yi?
Yaatal probabilite yu bari yu nekk ci suufu 1 mën na wàcci ba amul dara, kon jël log yi ak somme dafay tax math bi nekk bu dëgër.
Lan mooy jafe-jafe bi Laplace (yokkum-benn) di saafara?
Ku amul nooy, baat buñu musul gis ak benn klaas du jox klaas boobu probabilite nul; yokk-benn lim moytu lii.
Ban xeetu Naive Bayes moo gëna baax ci man-mani lim baat ci biir këyit?
Multinomial Naive Bayes dafay wane lim yu wuute yu melni ñaata yoon la baat bu nekk di feeñ, moo tax mu nekk standard ci bind.
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