3-2 essay: rule-based classifiers vs. decision tree models

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.

Please ensure you refer to the rubric for specific details on the requirements for this assignment!

Rubric:

Compare techniques: Explains the similarities and basic uses or concepts of each technique. (30%)

Contrast techniques: Explains the differences and contrasting uses or concepts of each technique. (30%)

Real-world example: Described 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. (10%)

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