Comparison of different machine learning algorithms in classification

Abstract

Classification, as a basic problem in the field of data analysis and machine learning, plays a more and more important role in human life. People are faced with huge amounts of information every day. How to classify information and how to extract useful information has gradually become a hot topic for scholars. There are many kinds of classifiers, such as neural network, support vector machine, decision tree, Bayesian classification algorithm and so on. This paper compares the classification results and accuracy of decision tree, support vector machine and naive Bayesian method by selecting data sets, and briefly describes its operation principle. The results show that the three machine learning classifiers perform well in dealing with the binary classification problem, but compared with the other two models, the decision tree model has higher accuracy.