Performance Evaluation of Classifier Models Using Resampling Techniques

Swarnalatha Purushotham, Dr.S. Prabu


One of the important datamining function is prediction. Many predictive models can be built for the data. The data may be continous, categorical or combination of both. For either of the above type of data many similar predictive models are available. So its highly important to choose the possible best accurate predictive model for the user data . For this the models are evaluated using resampling techniques. The evaluated models gives statistical results respectively. These statistical results are analysed and compared .The appropriate model that gives maximum accuracy for the user data is used to do predictions for further data of same type. The predictions thus made by the best model can be visualized. They form the decision reports for the user data.


Dataset, Resampling Technique, Cross validation, Accuracy, Class label, Training data, Test data, Model induction, Model deduction.

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