Multinomial Naive Bayes Algorithm. Multimodal naive bayes is a specialized version of naive bayes designed to handle text documents using word counts as it s underlying method of calculating probability. For example if a feature vector has n elements and each of them can assume k different values with probability pk then.
Multinomial naïve bayes consider a feature vector where a given term represents the number of times it appears or very often i e. Combining probability distribution of p with fraction of documents belonging to each class. For example if a feature vector has n elements and each of them can assume k different values with probability pk then.
Naive bayes classifier algorithm is a family of probabilistic algorithms based on applying bayes theorem with the naive assumption of conditional independence between every pair of a feature.
Multinomial naïve bayes consider a feature vector where a given term represents the number of times it appears or very often i e. At last gaussian is based on continuous distribution. Combining probability distribution of p with fraction of documents belonging to each class. In order to avoid underflow we will use the sum of logs.
