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)
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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.

  1.  What are the features, and targets in this training dataset? How many classes does this example have?
  2. 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.
  3. Is Bayesian classifier a supervised or unsupervised learning? Explain why.
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