Can Naive Bayes be used for text classification?

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.

How does Bayes theorem classify text?

2. The Naive Bayes algorithm

1. Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem.
2. The dataset is divided into two parts, namely, feature matrix and the response/target vector.
3. Naive Bayes assumes that each feature/variable of the same class makes an:
4. contribution to the outcome.

Why does Naive Bayes work well with text classification?

Since a Naive Bayes text classifier is based on the Bayes’s Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.

How is naive Bayes used to detect spam?

Naive Bayes classifiers are a popular statistical technique of e-mail filtering. Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes’ theorem to calculate a probability that an email is or is not spam.

Why do Multinomials 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.

Which of following is best algorithm for text classification?

Linear Support Vector Machine
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.

Which model is best for multiclass classification?

Popular algorithms that can be used for multi-class classification include: k-Nearest Neighbors. Decision Trees. Naive Bayes….Multi-Class Classification

• Face classification.
• Plant species classification.
• Optical character recognition.

What does “naive” Bayes mean in machine learning?

A naive Bayes classifier is an algorithm that uses Bayes’ theorem to classify objects. Naive Bayes classifiers assume strong, or naive, independence between attributes of data points. Popular uses of naive Bayes classifiers include spam filters, text analysis and medical diagnosis. These classifiers are widely used for machine learning because they are simple to implement. Naive Bayes is also known as simple Bayes or independence Bayes.

What makes naive Bayes classification so naive?

Naive Bayes is so ‘naive’ because it makes assumptions that are virtually impossible to see in real-life data and assumes that all the features are independent. Let’s take an example and implement the Naive Bayes Classifier, here we have a dataset that has been given to us and we’ve got a scatterplot which represents it.

When to use naive Bayes classifier?

Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.

What is “naive” in a naive Bayes classifier?

The first assumption of a Naive Bayes classifier is that the value of a particular feature is independent of the value of any other feature. Which means that the interdependencies within data are comfortably neglected. Hence the name ‘naive.’