6 Easy Steps to Learn Naive Bayes Algorithm.docx - 6 Easy Steps to Learn Naive Bayes Algorithm(with codes in Python and R SUNIL RAY Note This article

# 6 Easy Steps to Learn Naive Bayes Algorithm.docx - 6 Easy...

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For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’. Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. Bayes theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x) and P(x|c). Look at the equation below: Above, P ( c|x ) is the posterior probability of class (c, target ) given predictor (x, attributes ).
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