Question 4 Consider the learning task represented by the training example of the following table. Example Comedy Doctors Lawyers Guns Likes 1 false true false false false 2 True False True False True 3 False False True True True 4 False False True False False 5 False False False True False 6 True False False True False 7 True False False False True 8 False True True Ture True 9 False True True False False 10 True True Ture False True 11 True True False True False 12 False False False False False Suppose we have a system that observes a person’s TV watching habits to recommend other TV shows the person may like. Suppose that we have characterized each show by whether it is a comedy, doctors, lawyers, or guns. The table shows a training set telling whether the person likes various TV shows or not. What are the features, and targets in this training dataset? How many classes does this example have? Use Bayesian classifier, on the training dataset, to predict whether the user will like or not like the TV show with the attributes Comedy = true, Doctors = true, Lawyers = False, Guns = False. Is Bayesian classifier a supervised or unsupervised learning? Explain why.

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

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Question 4

Consider the learning task represented by the training example of the following table.

Example	Comedy	Doctors	Lawyers	Guns	Likes
1	false	true	false	false	false
2	True	False	True	False	True
3	False	False	True	True	True
4	False	False	True	False	False
5	False	False	False	True	False
6	True	False	False	True	False
7	True	False	False	False	True
8	False	True	True	Ture	True
9	False	True	True	False	False
10	True	True	Ture	False	True
11	True	True	False	True	False
12	False	False	False	False	False

Suppose we have a system that observes a person’s TV watching habits to recommend other TV shows the person may like. Suppose that we have characterized each show by whether it is a comedy, doctors, lawyers, or guns. The table shows a training set telling whether the person likes various TV shows or not.

What are the features, and targets in this training dataset? How many classes does this example have?
Use Bayesian classifier, on the training dataset, to predict whether the user will like or not like the TV show with the attributes Comedy = true, Doctors = true, Lawyers = False, Guns = False.
Is Bayesian classifier a supervised or unsupervised learning? Explain why.

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