-----------------------------------------------------
| description
-----------------------------------------------------
A | is a mail belonging to folder F_1
-----------------------------------------------------
B | is a mail belonging to folder F_2
-----------------------------------------------------
C | has a mail been classified before
-----------------------------------------------------
P | will a mail be classified to F_2
-----------------------------------------------------
Let’s assume that a mail that belonging to folder F_1, is also belonging to folder F_2, and has
been classified before. We want to predict the probability that the mail will be classified to F_2:
Pr{P=T|A=T,B=T,C=T}=Pr{A=T,B=T,C=T|P=T}Pr{T}/Pr{A=T,B=T,C=T}
Pr{P=F|A=T,B=T,C=T}=Pr{A=T,B=T,C=T|P=F}Pr{F}/Pr {A=T,B=T,C=T}
One of the easiest ways to compute an event’s probability is to take its frequency count.
In our table for example, all A,B,C events happened 20 times, event A happened 5 times, event B - 12, event c - 3.
Pr{A}=5/20; Pr{B}=12/20; Pr{C}=3/20.
Pr{A or B } = Pr{A} + Pr{B} – Pr{A and B}
Pr{A and B} = Pr{A}Pr{B|A} = Pr{B} Pr{A|B} - Bayes' rule
output attribute could be either T - true or F -false.
Something like that.