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The subject of classification is one of the important issues in different sciences. Logistic regression is one of the statistical
methods to classify data in which the underlying distribution of the data is assumed to be known. Today, researchers in
addition to statistical methods use other methods such as machine learning in which the distribution of the data does not
need to be known. In this paper, in addition to the logistic regression, some machine learning methods including CART
decision tree, random forest, Bagging and Boosting of supervising learning are introduced. Finally, using four real data
sets, we compare the performance of these algorithms with respect to the accuracy measure.