Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K) ID Marital Refund Тахable Cheat Status Income 1 Single Yes 125K No 2 Married No 100K No 3 Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes Married No 75K No 10 Single No 90K Yes 00

Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter9: Decision Making Under Uncertainty
Section9.2: Elements Of Decision Analysis
Problem 1P
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Train an ID3 decision tree for a dataset shown in the following table. The table contains 2
categorical attributes (refund and marital status) and 1 continuous attribute (taxable income).
Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes,
Divorced, 120K)
ID
Marital
Refund
Тахable
Cheat
Status
Income
1
Single
Yes
125K
No
2
Married
No
100K
No
3
Single
No
70K
No
4
Married
Yes
120K
No
Divorced
No
95K
Yes
Married
No
60K
No
7
Divorced
Yes
220K
No
8
Single
No
85K
Yes
Married
No
75K
No
10
Single
No
90K
Yes
00
Transcribed Image Text:Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K) ID Marital Refund Тахable Cheat Status Income 1 Single Yes 125K No 2 Married No 100K No 3 Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes Married No 75K No 10 Single No 90K Yes 00
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