Comparison and analysis of the accuracy of multiple machine learning algorithms in the field of spam classification

With the continuous advancement of science and technology, network data dissemination technology has been rapidly developed, but at the same time, the problem of spam is becoming more and more serious. This paper aims to analyse the classification principle of each machine learning classifier and compare the differences in the classification effect of each classifier. For the classification of spam, this paper mainly does the following aspects of the work: first, the selection of mail datasets and the pre-processing of email text information, the second is the extraction of the text characteristics of the email, The data is then divided into two parts, one for training and one for testing, using each machine to train the model, using the test set to test the model. Finally, parameters such as accuracy, precision, recall, and F1 score are calculated according to the mail classification results, comparing each model. The results show that the best classification effect is the neural network model and the naïve Bayes model, which can obtain better generalization ability in the test data and should be applied more in practice.