https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html

Gaussian Naive Bayes Scikit Reference

Gaussian Naive Bayes is a Naive Bayes classifier for continuous features. It assumes that each feature's values within each class follow a Gaussian distribution. During training, it estimates the mean and variance of each continuous feature separately for each class.

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The primary types of Naive Bayes classifiers are Bernoulli Naive Bayes, Multinomial Naive Bayes, and Gaussian Naive Bayes.

Types of Naive Bayes Classifiers

In the Bernoulli model, features are independent booleans/binary variables describing inputs. This model is popular for document classification tasks such as word presence, where binary term occurrence features are used rather than term frequencies.

Bernoulli Naive Bayes

With a multinomial model, samples represent the frequencies where certain events have been generated by a multinomial. Typically used for document classification, where there are discrete features like word counts.

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