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Classification—A Two-Step Process

• Model construction: describing a set of predetermined classes
• Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute
• The set of tuples used for model construction is training set
• The model is represented as classification rules, decision trees, or mathematical formulae
• Model usage: for classifying future or unknown objects
• Estimate accuracy of the model
• The known label of test sample is compared with the classified result from the model
• Accuracy rate is the percentage of test set samples that are correctly classified by the model
• Test set is independent of training set (otherwise overfitting)
• If the accuracy is acceptable, use the model to classify new data
• Note: If the test set is used to select models, it is called validation (test) set