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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

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
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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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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