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6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule should correspond to a branch in the decision tree. 7. Rule Application: Apply your rule-based system to the following instance: Color: Green, Size: Small, Temperature: 24.0, Weight: 260 8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement. 9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization. Problem 1 Exercises: Rule-Based Color Size Temperature Weight Class Sample (Nominal) (Ordinal) (Continuous) (Discrete) (Target) 1 Red Large 25.5 350 Positive 2 Blue Small 18.0 200 Negative 3 Green Medium 22.3 280 Positive 4 Red Small 19.8 210 Negative 10 5 Blue Large 28.1 400 Positive 6 Green Medium 23.5 320 Positive 7 Red Large 26.8 370 Positive 8 Blue Small 17.2 180 Negative 9 Green Medium 21.0 250 Negative 10 Red Small 20.5 230 Positive 1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples into either the positive or negative class based on the given attributes (Color, Size, Temperature, Weight). 2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under which a sample is classified as positive or negative. 3. Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification. 4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how confident are you in classifying a sample as positive based on the rules? 5. Rule Expansion: Add a new rule to your rule-based system.

6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule should correspond to a branch in the decision tree. 7. Rule Application: Apply your rule-based system to the following instance: Color: Green, Size: Small, Temperature: 24.0, Weight: 260 8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement. 9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization. Problem 1 Exercises: Rule-Based Color Size Temperature Weight Class Sample (Nominal) (Ordinal) (Continuous) (Discrete) (Target) 1 Red Large 25.5 350 Positive 2 Blue Small 18.0 200 Negative 3 Green Medium 22.3 280 Positive 4 Red Small 19.8 210 Negative 10 5 Blue Large 28.1 400 Positive 6 Green Medium 23.5 320 Positive 7 Red Large 26.8 370 Positive 8 Blue Small 17.2 180 Negative 9 Green Medium 21.0 250 Negative 10 Red Small 20.5 230 Positive 1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples into either the positive or negative class based on the given attributes (Color, Size, Temperature, Weight). 2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under which a sample is classified as positive or negative. 3. Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification. 4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how confident are you in classifying a sample as positive based on the rules? 5. Rule Expansion: Add a new rule to your rule-based system.

C++ for Engineers and Scientists
C++ for Engineers and Scientists
4th Edition
ISBN:9781133187844
Author:Bronson, Gary J.
Publisher:Bronson, Gary J.
Chapter12: Adding Functionality To Your Classes
Section12.5: Virtual Functions
Problem 5E
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Rule based problem

6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule should correspond to a branch in the decision tree. 7. Rule Application: Apply your rule-based system to the following instance: Color: Green, Size: Small, Temperature: 24.0, Weight: 260 8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement. 9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization.
Transcribed Image Text:6. Rule Visualization: Visualize your rule-based system in a decision tree format. Each rule should correspond to a branch in the decision tree. 7. Rule Application: Apply your rule-based system to the following instance: Color: Green, Size: Small, Temperature: 24.0, Weight: 260 8. Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement. 9. Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization.
Problem 1 Exercises: Rule-Based Color Size Temperature Weight Class Sample (Nominal) (Ordinal) (Continuous) (Discrete) (Target) 1 Red Large 25.5 350 Positive 2 Blue Small 18.0 200 Negative 3 Green Medium 22.3 280 Positive 4 Red Small 19.8 210 Negative 10 5 Blue Large 28.1 400 Positive 6 Green Medium 23.5 320 Positive 7 Red Large 26.8 370 Positive 8 Blue Small 17.2 180 Negative 9 Green Medium 21.0 250 Negative 10 Red Small 20.5 230 Positive 1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples into either the positive or negative class based on the given attributes (Color, Size, Temperature, Weight). 2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under which a sample is classified as positive or negative. 3. Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification. 4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how confident are you in classifying a sample as positive based on the rules? 5. Rule Expansion: Add a new rule to your rule-based system.
Transcribed Image Text:Problem 1 Exercises: Rule-Based Color Size Temperature Weight Class Sample (Nominal) (Ordinal) (Continuous) (Discrete) (Target) 1 Red Large 25.5 350 Positive 2 Blue Small 18.0 200 Negative 3 Green Medium 22.3 280 Positive 4 Red Small 19.8 210 Negative 10 5 Blue Large 28.1 400 Positive 6 Green Medium 23.5 320 Positive 7 Red Large 26.8 370 Positive 8 Blue Small 17.2 180 Negative 9 Green Medium 21.0 250 Negative 10 Red Small 20.5 230 Positive 1. Rule-Based Classification: Using a rule-based approach, create rules to classify samples into either the positive or negative class based on the given attributes (Color, Size, Temperature, Weight). 2. Rule Interpretation: Interpret the rules you created in 1). Explain the conditions under which a sample is classified as positive or negative. 3. Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification. 4. Rule Confidence: Assign confidence levels to the rules you created in 1). For example, how confident are you in classifying a sample as positive based on the rules? 5. Rule Expansion: Add a new rule to your rule-based system.
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