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Data Science

19/10/2020 Client: jwhite211 Deadline: 3 days

 Classification: Basic Concepts


Decision Tree Induction


Bayes Classification Methods


Rule-Based Classification   


Assignment 4


1- Consider the data in the following table: 


  


TID 




Home Owner




Marital Status




Annual Income




Defaulted   Borrower


 


1




Yes




Single




[120 - < 150K]




No


 


2




No




Married




[90 - < 120K]




No


 


3




No




Single




[60 - < 90K]




No


 


4




Yes




Married




[120 - < 150K]




No


 


5




No




Divorced




[90 - < 120K]




Yes


 


6




No




Married




[60 - < 90K]




No


 


7




Yes




Divorced




[120 - < 150K]




No


 


8




No




Single




[90 - < 120K]




Yes


 


9




No




Married




[60 - < 90K]




No


 


10




No




Single




[90 - < 120K]




Yes


Let Defaulted Borrower be the class label attribute. 


a) Given a data tuple X = (Home Owner= No, Marital Status= Married, Income= $120K). What would a naive Bayesian classification of the Defaulted Borrower for the tuple be?


2- Consider the training example in the following table for a binary classification problem.


  


Customer ID




Gender




Car Type




Shirt Size




Class


 


1




M




Family




S




C0


 


2




M




Sports




M




C0


 


3




M




Sports




M




C0


 


4




M




Sports




L




C0


 


5




M




Sports




XL




C0


 


6




M




Sports




XL




C0


 


7




F




Sports




S




C0


 


8




F




Sports




S




C0


 


9




F




Sports




M




C0


 


10




F




Luxury




L




C0


 


11




M




Family




L




C1


 


12




M




Family




XL




C1


 


13




M




Family




M




C1


 


14




M




Luxury




XL




C1


 


15




F




Luxury




S




C1


 


16




F




Luxury




S




C1


 


17




F




Luxury




M




C1


 


18




F




Luxury




M




C1


 


19




F




Luxury




M




C1


 


20




F




Luxury




L




C1


a) Find the gain for Gender, Car Type, and Shirt Size.


b) Which attribute will be selected as the splitting attribute? 

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