In this chapter, we learned about classification using naive Bayes. This algorithm constructs tables of probabilities that are used to estimate the likelihood that new examples belong to various classes. The probabilities are calculated using a formula known as Bayes' theorem, which specifies how dependent events are related. Although Bayes' theorem can be computationally expensive to process, a simplified version that makes so-called "naive" assumptions about the independence of features is capable of being used with extremely large datasets.

The naive Bayes classifier is often used for text classification. To illustrate its effectiveness, we employed naive Bayes on a classification task involving filtering spam SMS messages. Preparing ...

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