State prediction method and device

Abstract

The invention discloses a state prediction method and device. The method comprises the following steps of: sampling target users; respectively generating a negative sample, a positive sample and a verification sample according to account information of lost and unlost users according to a recognized sampling moment; training a decision-making tree which is used for predicting user loss state afterthe sampling moment; inputting the verification sample into the trained decision-making tree model to obtain a predicted loss state; and if a correct recall rate obtained through carrying out calculation according to the predicted loss state and a practical loss state is not smaller than a threshold value, determining that the training of the decision-making tree model is completed, and carryingout user loss state prediction. According to the state prediction method and device, the decision-making tree model is trained by a training sample generated through sampling the target users, and user loss state prediction is carried out according to the trained decision-making tree model, so that the technical problems of relatively recognition efficiency low and reusing difficulty caused by user loss state prediction carried out through artificial experiences or established rules in the prior art are solved.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a state prediction method and device. Background technique [0002] With the development of the Internet, many Internet companies have a large number of paying customers, and the customer base is growing year by year. The after-sales service for customer maintenance is particularly important. The current after-sales service has two characteristics. One is customer problems. Many, complicated, messy, customer service turnover rate is high, in the face of this situation, most of the customer service do not know what to do, how to do it; second, customer service resources are limited, there are hundreds of maintenance accounts for a single customer service, and the work intensity of customer service is high , the work is difficult, and the quality of service is difficult to guarantee. [0003] Faced with this situation, it is particularly important to screen ...

AI Technical Summary

Problems solved by technology

[0005] For this reason, the first purpose of the present invention is to propose a state prediction method, by sampling the positive samples, verification samples and negative samples generated by the target user, the decision tree model is trained, and the sampling is performed according to the trained decision tree model. Prediction of user churn status after time, which solves the technical problem of low recognition efficiency and difficult reuse in the prior art of predicting user churn status through manual experience or formulating rules

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