## What is multinomial in Naive Bayes?

The Multinomial Naive Bayes algorithm is a Bayesian learning approach popular in Natural Language Processing (NLP). The program guesses the tag of a text, such as an email or a newspaper story, using the Bayes theorem. It calculates each tag’s likelihood for a given sample and outputs the tag with the greatest chance.

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

After my research, i found out Multinomial Naive Bayes and Bernoulli Naive Bayes is more often used for text classification. Bernoulli just cares about whether the word happens or not. Multinomial cares about the number of occurrence of the word.

**Why is it called multinomial Naive Bayes?**

The term Multinomial Naive Bayes simply lets us know that each p(fi|c) is a multinomial distribution, rather than some other distribution. This works well for data which can easily be turned into counts, such as word counts in text.

**What is Bernoulli document model?**

When classifying a test document, the Bernoulli model uses binary occurrence information, ignoring the number of occurrences, whereas the multinomial model keeps track of multiple occurrences. As a result, the Bernoulli model typically makes many mistakes when classifying long documents.

### What is a multinomial 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.

### What is the difference between Gaussiannb and MultinomialNB?

Gaussian NB: It should be used for features in decimal form. GNB assumes features to follow a normal distribution. 2. MultiNomial NB: It should be used for the features with discrete values like word count 1,2,3…

**What do you know about Bernoulli and multinomial density?**

When k is 2 and n is 1, the multinomial distribution is the Bernoulli distribution. When k is 2 and n is bigger than 1, it is the binomial distribution. When k is bigger than 2 and n is 1, it is the categorical distribution. The Bernoulli distribution models the outcome of a single Bernoulli trial.

**What is Bernoulli distribution used for?**

Use of the Bernoulli Distribution in Epidemiology In experiments and clinical trials, the Bernoulli distribution is sometimes used to model a single individual experiencing an event like death, a disease, or disease exposure. The model is an excellent indicator of the probability a person has the event in question.

## What is naive Bayes Bernoulli?

Bernoulli Naive Bayes. BernoulliNB implements the naive Bayes training and classification algorithms for data that is distributed according to multivariate Bernoulli distributions; i.e., there may be multiple features but each one is assumed to be a binary-valued (Bernoulli, boolean) variable.

## What is multinomial naive Bayes classifier?

Multivariate Bernoulli Event Model The Multivariate Event model is referred to as Multinomial Naive Bayes. When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier. However, there is another commonly used version of Naïve Bayes, called Gaussian Naive Bayes Classification.

**What is the probability of an event?**

This probability can be defined as the prior knowledge or belief i.e. the probability of an event computed before the collection of new data. This probability is revised as new information becomes available to produce more accurate results.

**What is the difference between multinomial naive Bayes and Bernoulli algorithm?**

Multinomial Naïve Bayes consider a feature vector where a given term represents the number of times it appears or very often i.e. frequency. On the other hand, Bernoulli is a binary algorithm used when the feature is present or not.