The Naive Bayes Classifier is a basic and effective classification method that aids in the development of fast machine learning models capable of making quick predictions. It's a probabilistic classifier, which means it makes predictions based on an object's probability.
Although intractable, the conditional probability can be determined using the joint probability. The Bayes Theorem establishes a consistent method for estimating conditional probability. The computation for Bayes Theorem in its simplest version is as follows: P(A|B) = P(B|A) * P(A) / P(A) / P(A) / P(A) / P(A) / P(A) (B)
where are datasets of all this available
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