Method and device used for updating classification model

Abstract

The present application discloses a method for updating a classification model. The classification model includes a predetermined number of original decision trees for class prediction according to user behavior data in network applications. The method includes: providing the user Incremental data within a predetermined period of time is obtained from the behavior data server, and a training sample set is extracted from it; according to the training sample set, a new number of decision trees is generated; according to the historical classification accuracy and the training sample set For this classification accuracy rate, select the predetermined number of decision trees from the new decision tree and the original decision tree to form an updated classification model. The present application also provides a device for updating a classification model. The method provided in this application can improve the efficiency of model training and realize rapid response to business; and because the historical classification accuracy is introduced when evaluating the classification effect of the decision tree, it can smooth the short-term fluctuation of data.

Description

technical field [0001] The present application relates to a classification model based on a decision tree, in particular to a method for updating the classification model. The present application also relates to a device for updating a classification model. Background technique [0002] With the development of Internet technology, a large number of network applications have emerged, such as social networking, online reading, etc. In order to recommend targeted information to users or perform necessary monitoring and management, network application providers usually need to The operation behavior in the application is classified and predicted according to the pre-set goals, such as: fraud or risk identification, user grouping, purchasing power identification, etc. In order to improve prediction efficiency and accuracy, classification models (also called classifiers) are usually used for classification prediction in most network applications. [0003] Among them, the random ...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a method for updating the classification model to solve the problems of low training efficiency and untimely update of the existing classification model update method

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