Abstract:In this paper, the precision of logistic regression, naïve-Bayes and linear data classification methods, with regard to the Area Under Curve (AUC) metric have been compared. The effect of the parameters including size of the dataset, kind of the independent attributes, number of the discrete attributes and their values have been investigated. From the results, it can be concluded that in datasets consisting of both discrete and continuous attributes, the AUC of the three mentioned classifiers are the same. With increasing the number of the discrete attributes, the AUC of logistic regression is increased and the precision related to this classifier become more than the other two classifiers. Also considering the impact of the discrete attributes it can be seen that with increasing the number of values in discrete attributes the AUC related to the logistic regression classifier increases and linear classifier's AUC decreases, but the AUC of the naïve-Bayes classifier remains constant. Therefore, the results of this research can help data miners in selecting the most efficient classifier by considering the characteristics of the datasets.