Classification→ATw。- 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) set6 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