Why do we use Gaussian distribution for Naive Bayes?

This extension of naive Bayes is called Gaussian Naive Bayes. Other functions can be used to estimate the distribution of the data, but the Gaussian (or Normal distribution) is the easiest to work with because you only need to estimate the mean and the standard deviation from your training data.

Can we use Naive Bayes for continuous data?

The first Naive Bayes model to incorporate continuous feature values used the Gaussian distribution to model its continuous features, and was called the Gaussian Naive Bayes model as a result. However, any arbitrary probability distribution can be used to model continuous features in place of the Gaussian distribution.

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

Summary. Naive Bayes is a generative model. (Gaussian) Naive Bayes assumes that each class follow a Gaussian distribution. The difference between QDA and (Gaussian) Naive Bayes is that Naive Bayes assumes independence of the features, which means the covariance matrices are diagonal matrices.

What is main difference between Bernoulli Naive Bayes & Gaussian Naive Bayes classifier?

2 Answers. We use algorithm based on the kind of dataset we have. Bernoulli Naive bayes is good at handling boolean/binary attributes,while Multinomial Naive bayes is good at handling discrete values and Gaussian naive bayes is good at handling continuous values.

What is Gaussian Naive Bayes and how does it work?

Gaussian Naive Bayes supports continuous valued features and models each as conforming to a Gaussian (normal) distribution. An approach to create a simple model is to assume that the data is described by a Gaussian distribution with no co-variance (independent dimensions) between dimensions.

What is Gaussian Naive Bayes in machine learning?

Gaussian Naive Bayes is a variant of Naive Bayes that follows Gaussian normal distribution and supports continuous data. Naive Bayes are a group of supervised machine learning classification algorithms based on the Bayes theorem. It is a simple classification technique, but has high functionality.

Why do Multinomials Naive Bayes?

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.

What is the naive Bayes algorithm used for?

Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles.

Why is naive Bayesian classification called naive?

Naive Bayesian classification is called naive because it assumes class conditional independence. That is, the effect of an attribute value on a given class is independent of the values of the other attributes.

What is naive Bayes in machine learning?

A Naive Bayes Classifier is a supervised machine-learning algorithm that uses the Bayes’ Theorem, which assumes that features are statistically independent.

What does Bayes’ theorem mean?

Bayes’ theorem is a theorem used to calculate the probability of something being true, false, or a certain way. Bayes’ theorem is an extension of logic. It expresses how a belief should change to account for evidence.