• Create text file for the following data and load data through Pandas libraries
• Build your own Naïve Bayes classifier model using steps below
• Class: P(C) = Nc/N
• e.g., P(No) = 7/10,
• For discrete attributes:
P(Ai | Ck) = |Aik|/ Nck
where |Aik| is number of instances having attribute Ai and belongs to class Ck
Examples:
P(Status=Married|No) = 4/7
P(Refund=Yes|Yes)=0
•
Normal distribution:
categoricalcategorical continuous class
2 ij2
One for each (Ai,ci) pair
( A
)2
1
iij
P( Ai | c j )
e
2 ij2
For (Income, Class=No):
Tid
Refund
Marital
Taxable
Evade
Status
Income
If Class=No
sample mean = 110
1
Yes
Single
125K
No
sample variance = 2975
2
No
Married
100K
No
• Once Trained, test your model for the
3
No
Single
70K
No
cases below
4
Yes
Married
120K
No
X1 = {Refund = Yes, Status = Divorced,
5
No
Divorced
95K
Yes
Income = 90K, Evade = ?}
6
No
Married
60K
No
X2 = {Refund = No, Status = Married,
7
Yes
Divorced
220K
No
Income = 60K, Evade = ?}
8
No
Single
85K
Yes
9
No
Married
75K
No
10
No
Single
90K
Yes
