Problem 3: Naïve Bayes The goal is to build a Naive Bayes classifier to predict the class label based on these attributes. Here's the dataset: Age Income Education Temperature ID (Continuous) (Ordinal) (Nominal) (Continuous) Outcome 1 28 Medium Bachelor's 75.5 Yes 2 35 55 High Master's 68.2 No 3 40 Low High School 80.0 Yes 4 25 Medium PhD 72.8 No 5 6 8& 45 High Bachelor's 78.1 Yes 30 Medium High School 69.5 No 7 38 Low Master's 77.3 Yes 8 22 High PhD 74.9 No 9 33 10 42 37 Medium High School 70.4 No Low Bachelor's 76.7 Yes 1. Prior Probability: Calculate the prior probabilities of each class (Outcome: Yes and No). 2. Likelihood Calculation: Calculate the likelihood probabilities for the 'Age' attribute for both classes (Hint: compute P(Age/Yes) and P(Age/No)). 3. Ordinal Likelihood: For the 'Income' attribute (ordinal), calculate the likelihood probabilities for each class. 4 4. Nominal Likelihood: For the 'Education' attribute (nominal), calculate the likelihood probabilities for each class. 5. Continuous Attribute Probability: For the 'Temperature' attribute (continuous), calculate the mean and standard deviation for each class. 6. Prediction for a New Instance: Given a new instance with Age=32, Income-Low, Education=Master's, Temperature=72.0, predict the class using the Naive Bayes classifier.
Problem 3: Naïve Bayes The goal is to build a Naive Bayes classifier to predict the class label based on these attributes. Here's the dataset: Age Income Education Temperature ID (Continuous) (Ordinal) (Nominal) (Continuous) Outcome 1 28 Medium Bachelor's 75.5 Yes 2 35 55 High Master's 68.2 No 3 40 Low High School 80.0 Yes 4 25 Medium PhD 72.8 No 5 6 8& 45 High Bachelor's 78.1 Yes 30 Medium High School 69.5 No 7 38 Low Master's 77.3 Yes 8 22 High PhD 74.9 No 9 33 10 42 37 Medium High School 70.4 No Low Bachelor's 76.7 Yes 1. Prior Probability: Calculate the prior probabilities of each class (Outcome: Yes and No). 2. Likelihood Calculation: Calculate the likelihood probabilities for the 'Age' attribute for both classes (Hint: compute P(Age/Yes) and P(Age/No)). 3. Ordinal Likelihood: For the 'Income' attribute (ordinal), calculate the likelihood probabilities for each class. 4 4. Nominal Likelihood: For the 'Education' attribute (nominal), calculate the likelihood probabilities for each class. 5. Continuous Attribute Probability: For the 'Temperature' attribute (continuous), calculate the mean and standard deviation for each class. 6. Prediction for a New Instance: Given a new instance with Age=32, Income-Low, Education=Master's, Temperature=72.0, predict the class using the Naive Bayes classifier.
Chapter3: Performing Calculations With Formulas And Functions
Section3.1: Formulas And Functions
Problem 8QC
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Transcribed Image Text:Problem 3: Naïve Bayes
The goal is to build a Naive Bayes classifier to predict the class label based on these attributes. Here's the dataset:
Age
Income
Education
Temperature
ID
(Continuous)
(Ordinal)
(Nominal)
(Continuous)
Outcome
1
28
Medium
Bachelor's
75.5
Yes
2
35
55
High
Master's
68.2
No
3
40
Low
High School
80.0
Yes
4
25
Medium
PhD
72.8
No
5
6
8&
45
High
Bachelor's
78.1
Yes
30
Medium
High School
69.5
No
7
38
Low
Master's
77.3
Yes
8
22
High
PhD
74.9
No
9
33
10
42
37
Medium
High School
70.4
No
Low
Bachelor's
76.7
Yes
1. Prior Probability: Calculate the prior probabilities of each class (Outcome: Yes and No).
2. Likelihood Calculation: Calculate the likelihood probabilities for the 'Age' attribute for both classes
(Hint: compute P(Age/Yes) and P(Age/No)).
3. Ordinal Likelihood: For the 'Income' attribute (ordinal), calculate the likelihood probabilities for each
class.
4
4. Nominal Likelihood: For the 'Education' attribute (nominal), calculate the likelihood probabilities for
each class.
5. Continuous Attribute Probability: For the 'Temperature' attribute (continuous), calculate the mean and
standard deviation for each class.
6. Prediction for a New Instance: Given a new instance with Age=32, Income-Low, Education=Master's,
Temperature=72.0, predict the class using the Naive Bayes classifier.
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