What is the difference between multinomial Naive Bayes and Naive Bayes?

What is the difference between multinomial Naive Bayes and Naive Bayes?

In summary, Naive Bayes classifier is a general term which refers to conditional independence of each of the features in the model, while Multinomial Naive Bayes classifier is a specific instance of a Naive Bayes classifier which uses a multinomial distribution for each of the features.

What is multinomial Naive Bayes used for?

Multinomial Naive Bayes algorithm is a probabilistic learning method that is mostly used in Natural Language Processing (NLP). The algorithm is based on the Bayes theorem and predicts the tag of a text such as a piece of email or newspaper article.

What is multinomial Naive Bayes classifier?

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. However, in practice, fractional counts such as tf-idf may also work.

Why Naive Bayes is faster?

Naive Bayes is fast because all it needs are the prior probability values that do not change and can be stored ahead of time. The same probability values are reused in while calculating the posterior. As mentioned by Sameera Bharadwaja H, it uses very basic oprations to calculate prior and class conditional probality.

What is the difference between Gaussian, multinomial and Bernoulli naive Bayes?

What is the difference between Gaussian, Multinomial and Bernoulli Naïve Bayes classifiers? Gaussian Naive Bayes is useful when working with continuous values which probabilities can be modeled using a Gaussian distribution:

When to use multinomial NB vs Bernoulli NB?

2.MultiNomial NB: It should be used for the features with discrete values like word count 1,2,3… 3.Bernoulli NB: It should be used for features with binary or boolean values like True/False or 0/1. Am I correct till this point?

Why does multinomial naive Bayes treat all words equally?

Naive Bayes treats all words equally regardless of how they are placed because it’s difficult to keep track of every single reasonable phrase in a language. Multinomial Naïve Bayes consider a feature vector where a given term represents the number of times it appears or very often i.e. frequency.

When to use multinomial or naive NB algorithms?

Suggest differences among the various types of NB algorithms. The variant of Naive Bayes you use depends on the data. If your data consists of counts, the multinomial distribution may be an appropriate distribution for the likelihood, and thus multinomial Naive Bayes is appropriate.