i looked into one of the post about naive bayes calulation of naive part
Predit the class label for instance (A=1,B=2,C=2) using naive Bayes classifcation.
Let C1 be class 1 and C2 be class 2.
For C1, by the assumption of Naive Bayesian Classifier, we have P(A=1,B=2,C=2∣C1)=P(A=1∣C1)⋅P(B=2∣C1)⋅P(C=2∣C1)
Take P(A=1∣C1) as an example. There are 4 training records of C1, among which there are 2 records with A=1. Therefore, P(A=1∣C1)=24. Similarly, you can calculate P(B=2∣C1) and P(C=2∣C1).
It is similar to calculate P(A=1,B=2,C=2∣C2).
My query is how can we calculate the NON Naive part of this ?
P(A=1,B=2,C=2∣C1)= P(A=1∣C1)⋅P(B=2∣C1)⋅P(C=2∣C1) -> Here the events are considered to be independent of each other
What if the events are dependent ? How should we calcualte in that case ?