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Illustrate any one classification technique for the data set .Show how we can Classify a new tuple with(Homeowner = yes ; Status =Employed; Income=Average)
Id Homeowner Status Income Defaulted
1 Yes Employed High No
2 No Business Average No
3 No Employed Low No
4 Yes Business High No
5 No Unemployed Average Yes
6 No Business Low No
7 Yes Unemployed High No
8 No Employed Average Yes
9 No Business Low No
10 No Employed Average Yes

## Illustrate any one classification technique for the above data set .Show how we can Classify a new tuple with(Homeowner = yes ; Status =Employed; Income=Average)

Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence

Marks: 10M

Year: May 2015

0  upvotes
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Homeowner    
P(Yes\No )=3/6 P(No\Yes )=3/3 P(Yes\Yes )=0
P(No\No)=4/6    
Status    
P(Employed \Yes)=2/3 P( Business\Yes)=0 P(Unemployed\Yes) =1/3
P(Employed \No)=2/6 P(Business\No)=4/6 P(Unemployed \No)=1/6
Income    
P(High\Yes)=0 P(Low\Yes)=0 P(Average\Yes)=3/3
P(High\No)=3/6 P(Low\No)=3/6 P(Average\No)=1/6
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P(Yes)=3/10

P(No)=7/10

P(X\Yes) x P(Yes)=$\text{P( yes\yes) x P(Employed\yes)x P(Average\yes) x P(Yes)}\\ =0 * 2/3 * 3/3 * 3/10\\ =0$

P(X\No) x P(No)= $\text{P( yes\no) x P(Employed\no)x P(Average\no) x P(no)} \\ =\text{3/6 * 2/6 * 1/6 * 7/10} \\ =0.0194 \\ =0 \lt0.0194$

Therefore the naive Bayesian classifier predicts Defaulted= “No” for sample X

0  upvotes

In P(X\No) x P(No) = P( yes\no) x P(Employed\no)x P(Average\no) x P(no) = * 3/7 * 2/7 * 1/7* * 7/10 = 0.0122

Isn't it supposed to be divided by 7?

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