## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required

# Training a naive Bayes classifier

Now that we can extract features from text, we can train a classifier. The easiest classifier to get started with is the `NaiveBayesClassifier` . It uses Bayes Theorem to predict the probability that a given feature set belongs to a particular label. The formula is:

`P(label | features) = P(label) * P(features | label) / P(features)`
• `P(label)` is the prior probability of the label occurring, which is the same as the likelihood that a random feature set will have the label. This is based on the number of training instances with the label compared to the total number of training instances. For example, if 60/100 training instances have the label, the prior probability of the label is 60 percent.
• `P(features | label)` is the ...

## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

No credit card required