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I am trying to do a simple Naive Bayes classification, but I am getting a probability greater than zero. What am I doing wrong? I have included my calculations below.

Step 1: Prior. Calculate the prior probability of the class, i.e., the probability we would assign to the class before seeing any evidence. What percentage of all customers have churned? 0.48

Step 2: Evidence. Calculate the likelihood of the evidence: how common is the evidence among all examples? What percentage of all customers have a BS210 handset? 0.12

Step 3: Likelihood. Calculate the likelihood of seeing the evidence, given the class. What percentage of customers with a BS210 handset have churned? 0.916666667

Step 4: Posterior. Calculate the posterior probability of the class for a particular example. Given a new customer with BS210 handset, what is the likelihood that the customer will churn? Posterior = (Likelihood * Prior)/Evidence 3.666666667

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Assuming that your numbers are calculated correctly, your Bayes formula has no problem. And yes, $3.6$ ($> 1$) cannot be correct.

From your explanation, I can guess that you might have gotten the "Likelihood" wrong:

Step 3: Likelihood. ... What percentage of customers with a BS210 handset have churned

should be: What percentage of customers have churned, given they had BS210.

$p(E|H) = p(\text{customer has churned}|\text{bought BS210})$.

Bayes is all about $p(A|B)$ or $p(B|A), after all.

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