Multinomial Naive Bayes Formula. The number of times a given term appears in a document. If a is a random variable then under naive bayes classification using bernoulli distribution it can assume only two values for simplicity let s call them 0 and 1.
The multinomial distribution. If a is a random variable then under naive bayes classification using bernoulli distribution it can assume only two values for simplicity let s call them 0 and 1. Term frequency is often normalized by dividing the raw term frequency by the document length.
The multinomial naive bayes classifier is suitable for classification with discrete features e g word counts for text classification.
The multinomial distribution normally requires integer feature counts. Class sklearn naive bayes multinomialnb alpha 1 0 fit prior true class prior none source naive bayes classifier for multinomial models. Typically models will use a smoothing technique and the one generally associated with multimodal naive bayes is the laplacian smoothing add one smoothing. Term frequency is often normalized by dividing the raw term frequency by the document length.
