The goal is to build a rule-based 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 High Master's 68.2 No 3 40 Low High School 80.0 Yes 4 25 Medium PhD 72.8 No LO 5 45 High Bachelor's 78.1 Yes 6 30 Medium High School 69.5 No 7 38 Low Master's 77.3 Yes 8 00 22 22 High PhD 74.9 No 9 33 Medium High School 70.4 No 10 10 42 Low Bachelor's 76.7 Yes 1. Initial Rule Generation: Generate an initial rule that classifies instances based on a single attribute with the highest information gain or Gini index. 2. Rule Expansion: Expand the rule from question 1 by incorporating another attribute to improve the classification accuracy. 3. Handling Continuous Attributes: Propose a method to handle continuous attributes in rule-based classification, especially for attributes like 'Age' and 'Temperature.' 6 4. Discrete Attribute Rule: Create a rule based on a discrete attribute with more than two categories, like 'Education.' 5. Rule Evaluation: Evaluate the rules generated so far on the training dataset. Identify any instances that are misclassified. 6. Pruning Rules: Discuss the concept of rule pruning and why it might be necessary in rule-based methods. Provide an example of when rule pruning could be beneficial. 7. Handling Missing Values: Discuss how rule-based methods handle instances with missing attribute values and propose a strategy to address missing values in this dataset.
The goal is to build a rule-based 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 High Master's 68.2 No 3 40 Low High School 80.0 Yes 4 25 Medium PhD 72.8 No LO 5 45 High Bachelor's 78.1 Yes 6 30 Medium High School 69.5 No 7 38 Low Master's 77.3 Yes 8 00 22 22 High PhD 74.9 No 9 33 Medium High School 70.4 No 10 10 42 Low Bachelor's 76.7 Yes 1. Initial Rule Generation: Generate an initial rule that classifies instances based on a single attribute with the highest information gain or Gini index. 2. Rule Expansion: Expand the rule from question 1 by incorporating another attribute to improve the classification accuracy. 3. Handling Continuous Attributes: Propose a method to handle continuous attributes in rule-based classification, especially for attributes like 'Age' and 'Temperature.' 6 4. Discrete Attribute Rule: Create a rule based on a discrete attribute with more than two categories, like 'Education.' 5. Rule Evaluation: Evaluate the rules generated so far on the training dataset. Identify any instances that are misclassified. 6. Pruning Rules: Discuss the concept of rule pruning and why it might be necessary in rule-based methods. Provide an example of when rule pruning could be beneficial. 7. Handling Missing Values: Discuss how rule-based methods handle instances with missing attribute values and propose a strategy to address missing values in this dataset.
Chapter1: A First Program Using C#
Section: Chapter Questions
Problem 7RQ: The technique of packaging an objects attributes into a cohesive unit that can be used as an...
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Transcribed Image Text:The goal is to build a rule-based 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
High
Master's
68.2
No
3
40
Low
High School
80.0
Yes
4
25
Medium
PhD
72.8
No
LO
5
45
High
Bachelor's
78.1
Yes
6
30
Medium
High School
69.5
No
7
38
Low
Master's
77.3
Yes
8
00
22
22
High
PhD
74.9
No
9
33
Medium
High School
70.4
No
10
10
42
Low
Bachelor's
76.7
Yes
1. Initial Rule Generation: Generate an initial rule that classifies instances based on a single attribute with
the highest information gain or Gini index.
2. Rule Expansion: Expand the rule from question 1 by incorporating another attribute to improve the
classification accuracy.
3. Handling Continuous Attributes: Propose a method to handle continuous attributes in rule-based
classification, especially for attributes like 'Age' and 'Temperature.'
6
4. Discrete Attribute Rule: Create a rule based on a discrete attribute with more than two categories, like
'Education.'
5. Rule Evaluation: Evaluate the rules generated so far on the training dataset. Identify any instances that
are misclassified.
6. Pruning Rules: Discuss the concept of rule pruning and why it might be necessary in rule-based methods.
Provide an example of when rule pruning could be beneficial.
7. Handling Missing Values: Discuss how rule-based methods handle instances with missing attribute
values and propose a strategy to address missing values in this dataset.
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