Classification naïve bayésienne multinomiale

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Multinomial naïve Bayes

With a multinomial event model, samples (feature vectors) represent the frequencies with which certain events have been generated by a multinomial {\displaystyle (p_{1},\dots ,p_{n})}(p_1, \dots, p_n) where {\displaystyle p_{i}}p_{i} is the probability that event i occurs (or K such multinomials in the multiclass case). A feature vector {\displaystyle \mathbf {x} =(x_{1},\dots ,x_{n})}{\mathbf {x}}=(x_{1},\dots ,x_{n}) is then a histogram, with {\displaystyle x_{i}}x_{i} counting the number of times event i was observed in a particular instance. This is the event model typically used for document classification, with events representing the occurrence of a word in a single document (see bag of words assumption). The likelihood of observing a histogram x is given by

Source : Wikipedia Machine Learning