Predicting class labels Based on the user's attributes, behavior, location and characteristics, the decision tree algorithm, regression algorithm, etc. are used to mine the relevant bulk sms service characteristics of the user, and tap their potential needs. According to these potential needs, the users are tagged and pushed with different marketing strategies. For example, according to a user's consumption habits, his preference for commodities can be judged; according to the user's behaviors such as returning bad reviews, the risk level can bulk sms service be predicted. General statistics and rule labels can meet application requirements and occupy a large proportion in the development process.
Machine learning mining labels are mostly used for prediction bulk sms service scenarios, such as judging user risks, user purchase preferences, user churn intentions, etc., which have a long development cycle and high development costs. For example, a large amount of text data such as articles and posts related to the subject of data products has been accumulated on . Due to historical reasons, these articles have not been classified or labeled with corresponding labels, which is inconvenient to manage the content. Now you need to bulk sms service hashtag your posts accordingly. First, according to the type of articles that have been delineated, the articles that have been classified will be automatically divided into corresponding types. The second is to support the intensive management of articles, and automatically reward each article with tags related to its theme according to the content of the article (1)
Feature selection and development process Data bulk sms service classification: Manually mark a batch of documents accurately, as a training set sample, and a batch of documents that are not marked as a test set Data preprocessing: perform word segmentation on the text of the test set and training set, establish a corpus, and remove stop words, modal particles, etc. Bayes classification: classify articles from bulk sms service three aspects: precision, recall, and F-measure (2) Calculate the label weight Different behaviors of users on the platform have different weights at the user label level. For example, the user's behavior of buying a certain product is more important than the user's behavior of adding a shopping cart, saving a certain product, and browsing a certain product. User portraits