Naive Bayes is a type of classifier that employs the Bayes Theorem. It calculates membership probabilities for each class, such as the likelihood that a given record or data point belongs to a specific class. The most likely class is the one with the greatest probability.
Naive Because it presupposes class conditional independence, Bayes classification is referred to as naive. The effect of an attribute value on a specific class is independent of the other attributes' values. This assumption is made to reduce computational costs and hence is considered Naive.
Predicting the class of the test data set is simple and quick. It also excels at multi-class prediction. When the assumption of independence is met, a Naive Bayes classifier outperforms alternative models such as logistic regression, and less training data is required.
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