You are designing a Naïve Bayes classifier to predict whether a customer will buy a computer (Buy_Computer = Yes or No) based on a feature tuple
Bayes’ theorem gives the posterior probability:

When classifying a new customer, why is the division by P(X) often omitted when comparing P(Buy_Computer = Yes | X) and P(Buy_Computer = No | X)?
Select one:

Question

You are designing a  Naïve Bayes classifier  to predict whether a customer will buy a computer (Buy_Computer = Yes or No) based on a feature tuple   
 Bayes’ theorem gives the posterior probability: 
  
 When classifying a new customer, why is the division by  P(X)  often omitted when comparing P(Buy_Computer = Yes | X) and P(Buy_Computer = No | X)? 
 Select one:

Answer

Naïve Bayes inherently ignores normalization: The classifier only uses the product of likelihoods and priors, so normalization by P(X) is unnecessary for prediction.

Answer

P(X) is constant across all classes for a given X: The marginal probability of the features is the same for every class.  Therefore, it does not change the relative ordering of posterior probabilities and can be ignored for classification.

Answer

P(X) is always equal to 1: Since probabilities are normalized automatically, dividing by P(X) has no effect on the outcome.

Answer

P(X) is usually very small: Because feature combinations are rare, P(X) is near zero, so including it would not materially affect which class has higher posterior probability.

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You are designing a Naïve Bayes classifier to predict whether a customer will ...

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