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The classification rules can be applied to the new data tuples if the accuracy is considered acceptable. Using Classifier for Classification. The classifier is built from the training set made up of database tuples and their associated class labels. Interpretability − It refers to what extent the classifier or predictor understands. In both of the above examples, a model or classifier is constructed to predict the categorical labels. Some suggestions: Go back to the last page Go to the home page .. Speed − This refers to the computational cost in generating and using the classifier or predictor. 403 Forbidden . What is classification?.
Generalization − The data can also be transformed by generalizing it to the higher concept. Therefore the data analysis task is an example of numeric prediction. What is prediction?. Classification models predict categorical class labels; and prediction models predict continuous valued functions. Normalization involves scaling all values for given attribute in order to make them fall within a small specified range. Classification and Prediction Issues. In this case, a model or a predictor will be constructed that predicts a continuous-valued-function or ordered value. A bank loan officer wants to analyze the data in order to know which customer (loan applicant) are risky or which are safe. Each tuple that constitutes the training set is referred to as a category or class. Data Transformation and reduction − The data can be transformed by any of the following methods.
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