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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 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. able[[age,income,student,credit_rating,Buys_computer],[<=30,high,no,Fair,No],[<=30,high,no,excellent,No],[31dots40,high,no,fair,Yes],[>40,low,no,fair,Yes],[>40,low,yes,fair,Yes],[>40,low,yes,excellent,No],[<=30,medium,no,fair,No],[<=30,low,yes,fair,Yes],[>40,medium,yes,fair,Yes],[<=30,medium,yes,excellent,Yes],[31dots40,medium,no,excellent,Yes],[31dots40,high,yes,fair,Yes],[>40,medium,no,excellent,No]] The following table gives a training data set of 14 class labeled tuples. The class Iabel attribute, buys a computer, has two distinct values (namely {yes, no}). Given an unknown tuple X = (age <= 3o, 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 <=30 high no Fair No <=30 high no excellent No 31...40 high no fair Yes >40 low no fair Yes >40 low yes fair Yes >40 low yes excellent No <=30 medium no fair No <=30 low yes fair Yes >40 medium yes fair Yes <=30 medium yes excellent Yes 31...40 medium no excellent Yes 31...40 high yes fair Yes >40 medium no excellent 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  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.
	able[[age,income,student,credit_rating,Buys_computer],[<=30,high,no,Fair,No],[<=30,high,no,excellent,No],[31dots40,high,no,fair,Yes],[>40,low,no,fair,Yes],[>40,low,yes,fair,Yes],[>40,low,yes,excellent,No],[<=30,medium,no,fair,No],[<=30,low,yes,fair,Yes],[>40,medium,yes,fair,Yes],[<=30,medium,yes,excellent,Yes],[31dots40,medium,no,excellent,Yes],[31dots40,high,yes,fair,Yes],[>40,medium,no,excellent,No]]
The following table gives a training data set of 14 class labeled tuples. The class Iabel attribute, buys a computer, has two distinct values (namely {yes, no}). Given an unknown tuple X = (age <= 3o, 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 <=30 high no Fair No <=30 high no excellent No 31...40 high no fair Yes >40 low no fair Yes >40 low yes fair Yes >40 low yes excellent No <=30 medium no fair No <=30 low yes fair Yes >40 medium yes fair Yes <=30 medium yes excellent Yes 31...40 medium no excellent Yes 31...40 high yes fair Yes >40 medium no excellent 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 age 30 income 61313

Added by Francisco Jose Y.

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