Rule-Based Classifiers vs. Decision Tree Models

  • by

Rule-Based Classifiers vs. Decision Tree Models

ITS 632 Module Three Essay Guidelines and Rubric
Topic: Rule-Based Classifiers vs. Decision Tree Models
Overview: The purpose of this assignment is to determine which method is more appropriate in certain scenarios for building classification models relative to data mining practices. Classification is a pervasive data mining problem which has many applications, such as medical analysis, fraud detection, and network security. Various types of classification approaches have been proposed to address research problems. Classification is generally divided into two steps. First, construct a classification model based on the training dataset. Second, use the model to predict new instances for which the class labels are unknown. Hence, classification divides data samples into target classes. The classification technique predicts the target class for each data point. For example, patient can be classified as “high risk” or “low risk” patient based on their disease pattern using data classification approach. It is a supervised learning approach having known class categories.
 Compare and contrast Rule-Based Classifiers vs. Decision Tree Models. For example, a training dataset is not required with rule-based classifiers, but this method is difficult to work with due to all the rules that must be listed.
 In what situations is it better to use Rule-Based Classifiers rather than a Decision Tree model? Are they mutually exclusive techniques?
 Please provide a real-world example to support your inferences.
Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-4 pages in length (excluding title page, references, and appendices) and include at least two credible scholarly references to support your findings. The UC Library is a good place to find these sources. Be sure to cite and reference your work using the APA guides and essay template that are located in the courseroom.
Include the following critical elements in your essay:
I. Compare: Compare the process of building classification models with the rule-based technique and the decision tree technique. How are the processes for pre-mining and constructing the model similar? What are the benefits of using each model – are they similarly beneficial? Please provide an explanation of how these methods compare – no list, no tables.

II. Contrast: Contrast the two techniques and the resulting models in the context of decision-making. Do they have different levels of reliability and accuracy of the outcome(s)? Explain which technique is more reliable or accurate and discuss why. Please provide an explanation of how these methods are different – no list, no tables.
III. Real-world example: Please describe a practical application of how one of these techniques is used in business. For example, the classic application of association rule mining is the market basket data analysis, aiming to determine how items purchased by customers in a supermarket are associated or co-occurring together.
Required elements:  Please ensure your paper complies APA 6th edition style guidelines. There is an essay template located under the Information link.  APA basics:
o Your essay should be typed, double-spaced on standard-sized paper (8.5″ x 11″) o Use 1″ margins on all sides, first line of all paragraphs is indented ½” from the margin o Use 12 pt. Times New Roman font o Include and introduction and conclusion (at least one paragraph)
 Follow the outline provided above and use section headers to improve the readability of your paper. If I cannot read and understand it, you will not earn credit for the content.
Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value
Compare techniques Explains the similarities and basic uses or concepts of each technique. Comparison of the techniques lacks substantive explanation.
Did not present a valid comparison of the two techniques.
30
Contrast techniques
Explains the differences and contrasting uses or concepts of each technique.
Description of the differences between the techniques lacks substantive explanation.
Did not present a valid description of the differences between the two techniques.
30
Real-world example
Described one valid use case or practical example of how one of the techniques is used in business.
Description of the use case or practical example of how one of the techniques is used in business lacks substantive explanation.
Did not describe one valid use case or practical example of how one of the techniques is used in business.
30

Articulation of Response Submission has no major errors related to citations, grammar, spelling, syntax, or organization.
Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas.
Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas.
10
EARNED TOTAL 100%

The post Rule-Based Classifiers vs. Decision Tree Models appeared first on Best Custom Essay Writing Services | ourWebsite.

Rule-Based Classifiers vs. Decision Tree Models

 

Rule-Based Classifiers vs. Decision Tree Models  Do you need help with Rule-Based Classifiers vs. Decision Tree Models ? At Homework Geeks, we can take care of all your academic needs. we can write your papers, do your presentations, labs, discussion questions, and final exams too. Rule-Based Classifiers vs. Decision Tree Models 

Rule-Based Classifiers vs. Decision Tree Models 

Rule-Based Classifiers vs. Decision Tree Models 

Rule-Based Classifiers vs. Decision Tree Models

ITS 632 Module Three Essay Guidelines and Rubric
Topic: Rule-Based Classifiers vs. Decision Tree Models
Overview: The purpose of this assignment is to determine which method is more appropriate in certain scenarios for building classification models relative to data mining practices. Classification is a pervasive data mining problem which has many applications, such as medical analysis, fraud detection, and network security. Various types of classification approaches have been proposed to address research problems. Classification is generally divided into two steps. First, construct a classification model based on the training dataset. Second, use the model to predict new instances for which the class labels are unknown. Hence, classification divides data samples into target classes. The classification technique predicts the target class for each data point. For example, patient can be classified as “high risk” or “low risk” patient based on their disease pattern using data classification approach. It is a supervised learning approach having known class categories.
 Compare and contrast Rule-Based Classifiers vs. Decision Tree Models. For example, a training dataset is not required with rule-based classifiers, but this method is difficult to work with due to all the rules that must be listed.
 In what situations is it better to use Rule-Based Classifiers rather than a Decision Tree model? Are they mutually exclusive techniques?
 Please provide a real-world example to support your inferences.
Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-4 pages in length (excluding title page, references, and appendices) and include at least two credible scholarly references to support your findings. The UC Library is a good place to find these sources. Be sure to cite and reference your work using the APA guides and essay template that are located in the courseroom.
Include the following critical elements in your essay:
I. Compare: Compare the process of building classification models with the rule-based technique and the decision tree technique. How are the processes for pre-mining and constructing the model similar? What are the benefits of using each model – are they similarly beneficial? Please provide an explanation of how these methods compare – no list, no tables.

II. Contrast: Contrast the two techniques and the resulting models in the context of decision-making. Do they have different levels of reliability and accuracy of the outcome(s)? Explain which technique is more reliable or accurate and discuss why. Please provide an explanation of how these methods are different – no list, no tables.
III. Real-world example: Please describe a practical application of how one of these techniques is used in business. For example, the classic application of association rule mining is the market basket data analysis, aiming to determine how items purchased by customers in a supermarket are associated or co-occurring together.
Required elements:  Please ensure your paper complies APA 6th edition style guidelines. There is an essay template located under the Information link.  APA basics:
o Your essay should be typed, double-spaced on standard-sized paper (8.5″ x 11″) o Use 1″ margins on all sides, first line of all paragraphs is indented ½” from the margin o Use 12 pt. Times New Roman font o Include and introduction and conclusion (at least one paragraph)
 Follow the outline provided above and use section headers to improve the readability of your paper. If I cannot read and understand it, you will not earn credit for the content.
Critical Elements Proficient (100%) Needs Improvement (70%) Not Evident (0%) Value
Compare techniques Explains the similarities and basic uses or concepts of each technique. Comparison of the techniques lacks substantive explanation.
Did not present a valid comparison of the two techniques.
30
Contrast techniques
Explains the differences and contrasting uses or concepts of each technique.
Description of the differences between the techniques lacks substantive explanation.
Did not present a valid description of the differences between the two techniques.
30
Real-world example
Described one valid use case or practical example of how one of the techniques is used in business.
Description of the use case or practical example of how one of the techniques is used in business lacks substantive explanation.
Did not describe one valid use case or practical example of how one of the techniques is used in business.
30

Articulation of Response Submission has no major errors related to citations, grammar, spelling, syntax, or organization.
Submission has major errors related to citations, grammar, spelling, syntax, or organization that negatively impact readability and articulation of main ideas.
Submission has critical errors related to citations, grammar, spelling, syntax, or organization that prevent understanding of ideas.
10
EARNED TOTAL 100%

The post Rule-Based Classifiers vs. Decision Tree Models appeared first on Best Custom Essay Writing Services | ourWebsite.

Rule-Based Classifiers vs. Decision Tree Models

 

Leave a Reply

Your email address will not be published. Required fields are marked *