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A classification technique (or classifier) is a systematic approach used in building classification models from an input data set. The model generated by the learning algorithm should fit both the input data well and correctly predict the class labels of records it has never seen before. Therefore, a key objective of the learning algorithm is to build models with good generalization capability i.e. models that accurately predict the class labels of previously unknown records. The accuracy or error rate computed from the test set can also be used to compare the relative performance of different classifiers on the same domain.