Naïve Bayes Explained with Course Ratings Prediction Example in Python
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Bayes’ Theorem is a mathematical formula that allows us to calculate the ‘reversed’ conditional probabilities. It is often used when we have some prior belief about the probability of an event happening, and want to incorporate that information to calculate the conditional probability of an outcome. We could define the terms as follows:
Posterior = Likelihood * Prior / Evidence
Naïve Bayes applies the concept of Bayes Theorem to compute P(yi=1|xi), P(yi=2|xi) …. P(yi=k|xi) *from the value of *P(xi|yi=1), P(xi|yi=2)….P(xi|yi=k) and the value of P(x) & P(y=1)….P(y=k)
Our prediction is simply the y value that gives the maximum among all P(yi=1|xi), P(yi=2|xi) …. P(yi=k|xi).
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