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Classification Concept

 Classification is a form of data analysis that extracts models describing important data classes. The models predict categorical class labels for the given data. Fraud detection, Target marketing are few examples for classification.

The classification models are constructed through learning steps which is called training phase. 

The learning step builds the classifier by analyzing the training set made up of data sets and their associated class labels. The class label attribute is discrete-valued and unordered. As the class labels of training data are provided, the learning is called supervised learning. The accuracy of a classifier on a given test set is the percentage of test set that are correctly classified.

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