The Application of Machine Learning in Spam Filtering

With the wide application of the Internet, Spam is increasingly rampant, which seriously affects the normal communication and network environment of users. Aiming at this problem, this paper deeply studies the related technology of spam filter. Through the analysis of a large number of spam samples, a variety of effective features are extracted, such as keywords, word frequency, sentence structure and so on. Machine learning algorithms, such as logistic and naive Bayes, are used to construct efficient classification models. At the same time, the model is optimized and improved to improve the understanding and recognition accuracy of semantics. The experimental results show that the proposed filter has significant improvement in accuracy, recall rate and F1 value, which can effectively filter spam and provide users with a cleaner email environment, and has important practical application value.