The Naive Bayes classifier is based on the Bayes theorem's premise of conditional probability. When doing probability math, we commonly denote probability as P. The following are some of the probability in this event: The chance of receiving two heads is 1/4.
Given the class, naive Bayes is a simple learning technique that employs the Bayes rule along with the strong assumption that the attributes are conditionally independent. Despite the fact that this independence requirement is frequently violated in practice, naive Bayes often produces competitive classification accuracy.
Naive Bayes techniques are a type of supervised learning algorithms that employ Bayes' theorem with the "naive" assumption of conditional independence between every pair of features given the class variable value. It was first used for text categorization jobs and is still used as a benchmark today.
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