# How are conditional prob calculated for numeric variables in Naive Bayes

#1

hello,

For Naive Bayes in classification conditional probabilities for categories are calculated using the frequency values.For example in the below code:

``````nb <- naiveBayes(churn~.,churnTrain)
``````

on seeing the output:

So for P( churn = “yes” |intl_plan = “yes” ) the conditional prob is drawn from the counts in the dataset.

The result is the same as the part marked in red in image 1.
But for a numeric/continuous variable like number_vmail_messages how is this calculated??
Can someone please clarify this to me.??

#2

There are few assumptions for Naive Bayes classifier that can help you understand this:

• Independence of the predictor variables,
• Continuous variable has normal distribution

For continuous variables, we assumes distribution is normal and then calculate mean and standard deviation and after that z-value then look for probabilities in z-table hence probabilities can be estimated for each of your continuous variables to make the naive Bayes classifier.

Thx…

#3

Hi @Imran,

thanks a lot for the reply.

Could you also please explain how the z-scores are calculated.I mean why are there ,1 and ,2 as shown in the image above.Is the data being divided into two parts,under each class(yes/no)?
Also,the values shown are not prob values,so are these the z-scores??