What is the Naive Bayes Algorithm?

Bayes Theorem The Bayes Theorem is used to calculate conditional probability. The formula below can be used to determine the likelihood of an event occurring if event B has already occurred. https://www.sevenmentor.com/da....ta-science-course-in

P(A| = (P(B|A) * P(A)) / P(

During the initial training phase, an algorithm is created using the labeled data. Assume we have a dataset that has N instances and M attributes. The class label represents a target variable.

Prior probabilities (P(C),), The algorithm calculates P(C), which is the prior probability of a random instance that belongs to a specific class.

(C) each feature Fi with a class label. This is the probability of a particular feature being present if an instance belongs to a specific class.

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Bayes' theorem is used to determine the class of unknown instances.

P(C|X) = (P(X|C) * P(C)) / P(X)

Multiplication of the probability (P(xi), by the prior probability (P(C)).

Predicting Class: The algorithm chooses the class label that has the highest posterior probabilities based on a given example.

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