# Naive bayes

You can use the Naive Bayes algorithm to classify your data when features can be assumed independent.

You can use the Naive Bayes algorithm to classify your data when features can be assumed independent. The algorithm uses independent features to calculate the probability of a specific class. For example, you might want to predict the probability that an email is spam. In that case, you would use a corpus of words associated with spam to calculate the probability the email's content is spam.

You can use the following functions to build a Naive Bayes model, view the model, and use the model to make predictions on a set of test data:

For a complete example of how to use the Naive Bayes algorithm in Vertica, see Classifying data using naive bayes.