Rule-Based Classifiers vs. Decision Tree Models

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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 in the medical industry, patients 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.

*****Include the following critical elements in your essay:

  • 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.
  • 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.

  • 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

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. Be sure to cite and reference your work using the APA guides.

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