Why multinomial Naive Bayes is used in text classification?

Why multinomial Naive Bayes is used in text classification?

Multinomial Naive Bayes is one of the most popular supervised learning classifications that is used for the analysis of the categorical text data. Text data classification is gaining popularity because there is an enormous amount of information available in email, documents, websites, etc. that needs to be analyzed.

Can Naive Bayes be used for text classification?

Naive Bayes is a learning algorithm commonly applied to text classification. Some of the applications of the Naive Bayes classifier are: (Automatic) Classification of emails in folders, so incoming email messages go into folders such as: “Family”, “Friends”, “Updates”, “Promotions”, etc.

What is multinomial naive Bayes algorithm?

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.

Can multinomial Naive Bayes be used for binary classification?

Multinomial Naive Bayes Model with Python Implementation For binary classification, we aim to predict whether a record is a 1 or a 0 such as spam/not spam or churn/not churn and for multiclass classification, we aim to predict the class of a record such as classifying a mail as primary/social/promotional, etc.

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 the difference between Bernoulli and multinomial Naive Bayes?

Multinomial NB cares about counts for multiple features that do occur, whereas Bernoulli NB cares about counts for a single feature that do occur and counts for the same feature that do not occur.

Which Naive Bayes is best for text classification?

Bernoulli Naive Bayes This is best visualized with the help of a histogram. Different variations of the Naive Bayes classifier all work with the same analogy of independence of features.

What is the best algorithm for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79% which is 5% improvement over Naive Bayes.

What is Bernoulli Naive Bayes?

Summary. Bernoulli Naive Bayes is one of the variants of the Naive Bayes algorithm in machine learning. It is very useful to be used when the dataset is in a binary distribution where the output label is either present or absent.

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 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.

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