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.
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