The following table gives a training data set of 14 class labeled tuples. The class label...
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The following table gives a training data set of 14 class labeled tuples. The class label attribute, buys a computer, has two distinct values (namely {yes, no}). Given an unknown tuple X = (age <= 30, income = medium, student = yes, credit_rating = fair), the task is to classify the tuple X by the following approach. [Decision tree]: select attributes by information gain, compute ID3 decision tree, and classify the tuple X. age income student credit_rating | Buys_computer high no <=30 No <=30 high no No 31...40 high no low no low yes low yes medium no >40 >40 >40 <=30 <=30 low yes >40 medium yes <=30 medium yes 31...40 medium no 31...40 high >40 yes medium no Fair excellent fair fair fair excellent fair fair fair excellent excellent fair excellent Yes Yes Yes No No Yes Yes Yes Yes Yes No The following table gives a training data set of 14 class labeled tuples. The class label attribute, buys a computer, has two distinct values (namely {yes, no}). Given an unknown tuple X = (age <= 30, income = medium, student = yes, credit_rating = fair), the task is to classify the tuple X by the following approach. [Decision tree]: select attributes by information gain, compute ID3 decision tree, and classify the tuple X. age income student credit_rating | Buys_computer high no <=30 No <=30 high no No 31...40 high no low no low yes low yes medium no >40 >40 >40 <=30 <=30 low yes >40 medium yes <=30 medium yes 31...40 medium no 31...40 high >40 yes medium no Fair excellent fair fair fair excellent fair fair fair excellent excellent fair excellent Yes Yes Yes No No Yes Yes Yes Yes Yes No
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Based on the provided training dataset we can use the ID3 decision tree algorithm to classify th... View the full answer
Related Book For
Applied Regression Analysis and Other Multivariable Methods
ISBN: 978-1285051086
5th edition
Authors: David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg
Posted Date:
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