Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. Classification is a supervised machine learning algorithm which deals with identifying, to which of the set of categories a new observation belong on the basis of training set of data containing observations whose category membership is known. Decision tree builds classification or regression models in the form of tree structure. This paper focuses on comparison of ID3, CART, C4.5, C5.0 and random forest.