How do I use Naïve Bayes to predict a class label for a test sample $(A=1, B=1, C=1)$
I know Bayes Theorem is:
$$P(C|A) = [P(A|C) P(C)]/P(A)$$ I have no idea how to do this, please help.
2026-03-25 09:34:46.1774431286
Naive Bayes to Predict a class label
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Informally, what Bayes' rule here calculates is: "What is the probability that $C$ occurs if $A$ occurs?"
Now, you already have the formula, just plug in the numbers. $P(A)$ is the probability that event $A$ occurs. This is $P(A)=5/10=1/2$, just count it. Also, counting the frequencies $P(C)=5/10=1/2$.
$P(A|C)$ is just: what is the probability that $A$ occurs if $C$ occured? Counting again, $P(A|C)=3/5$. Putting together: $$P(C|A) = \frac{P(A|C) P(C)}{P(A)}=\frac{3/5 \cdot 1/2}{1/2}=3/5,$$ that is, 60%.