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 Single Married 1 Yes 125K No 2 No 100K No Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes 6. Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes 9. Married No 75K No 10 Single No 90K Yes 222

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 Single Married 1 Yes 125K No 2 No 100K No Single No 70K No 4 Married Yes 120K No Divorced No 95K Yes 6. Married No 60K No 7 Divorced Yes 220K No 8 Single No 85K Yes 9. Married No 75K No 10 Single No 90K Yes 222

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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