With the Naive Bayes classifier, why do we have to normalize the probabilities after calculating the probabilities of each hypothesis? In the Naive Bayes classifier, why do we have to normalize the probabilities after calculating the probabilities of each hypothesis?
 A: You do not have to normalize the probabilities if you only care about knowing which class ($\hat{y}$) your input ($\mathbf{x}=x_1, \dots, x_n$) most likely belongs to, since the maximum a posteriori (MAP) decision rule  is as follows:
$\hat{y} = \underset{k \in \{1, \dots, K\}}{\operatorname{argmax}}p(C_k \vert x_1, \dots, x_n) =  \underset{k \in \{1, \dots, K\}}{\operatorname{argmax}} \ p(C_k) \displaystyle\prod_{i=1}^n p(x_i \vert C_k)$
Since
$$\begin{align}
p(C_k \vert x_1, \dots, x_n) & \varpropto p(C_k, x_1, \dots, x_n) \\
                             & \varpropto p(C_k) \ p(x_1 \vert C_k) \ p(x_2\vert C_k) \ p(x_3\vert C_k) \ \cdots \\
                             & \varpropto p(C_k) \prod_{i=1}^n p(x_i \vert C_k)\,.
\end{align}$$
If you do want the class probabilities, then you indeed need to normalize:
$p(C_k \vert x_1, \dots, x_n) = ~\frac{p(C_k) \prod_{i=1}^n p(x_i \vert C_k)\,}{ \sum_{1 \leq j \leq |C|}{ \left( p(C_j) \prod_{i=1}^n p(x_i \vert C_j\,)   \right) }}$
But keep in mind that, from {1} 

the winning class in NB classification usually has a much larger probability than the other classes and the estimates diverge very significantly from  the true probabilities. NB classifiers
  estimate badly, but often classify well.


References:


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*{1} Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. "Introduction to Information Retrieval." 2009, chapter 13 Text classification and Naive Bayes.

