{"id":18673,"date":"2020-09-20T12:35:00","date_gmt":"2020-09-20T07:05:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/multinomial-naive-bayes-explained\/"},"modified":"2024-09-02T15:29:08","modified_gmt":"2024-09-02T09:59:08","slug":"multinomial-naive-bayes-explained","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/multinomial-naive-bayes-explained\/","title":{"rendered":"Multinomial Naive Bayes Explained"},"content":{"rendered":"\n
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  1. Introduction to Multinomial Na\u00efve Bayes <\/a><\/strong><\/li>\n\n\n\n
  2. The Bag of Words Model<\/a><\/strong><\/li>\n\n\n\n
  3. Difference between Bernoulli, Multinomial and Gaussian Na\u00efve Bayes<\/a><\/strong><\/li>\n\n\n\n
  4. Advantages<\/a><\/strong><\/li>\n\n\n\n
  5. Disadvantages<\/a><\/strong><\/li>\n\n\n\n
  6. Applications <\/a><\/strong><\/li>\n<\/ol>\n\n\n\n

    Contributed by: Gautam Solanki
    LinkedIn Profile:     https:\/\/www.linkedin.com\/in\/gautam-solanki-05b988a2\/ <\/a><\/em><\/p>\n\n\n\n

    Introduction<\/strong><\/h2>\n\n\n\n

    With an ever-growing amount of textual information stored in electronic form such as legal documents, policies, company strategies, etc., automatic text classification is becoming increasingly important. This requires a supervised learning technique that classifies every new document by assigning one or more class labels from a fixed or predefined class. It uses the bag of words approach, where the individual words in the document constitute its features, and the order of the words is ignored. This technique is different from the way we communicate with each other. It treats the language like it's just a bag full of words and each message is a random handful of them. <\/strong>Large documents have a lot of words that are generally characterized by very high dimensionality feature space with thousands of features. Hence, the learning algorithm requires to tackle high dimensional problems, both in terms of classification performance and computational speed. <\/p>\n\n\n\n

    Na\u00efve Bayes<\/a>, which is computationally very efficient and easy to implement, is a learning algorithm frequently used in text classification problems. Two event models are commonly used: <\/p>\n\n\n\n