Model training method and device based on risk identification and electronic equipment

Abstract

The embodiment of the invention discloses a model training method and device based on risk identification and electronic equipment. According to the specific scheme, the method comprises the steps ofobtaining a first data set without a sample label, wherein the first data set comprises sample data expected to have a first type of sample label, and the sample data expected to have the first type of sample label is doped with sample data with a second type of sample label; and pre-configuring a first type of sample tags for the first data set, and operating the target model configured with thefirst model parameters by utilizing the first data set to generate a prediction value; utilizing a loss function to judge the loss amount of the predicted value relative to a target value reflected bythe first data set; and estimating a statistical center estimated value of the first data set corresponding to the loss amount, converting the statistical center estimated value into a statistical center expected value, and adjusting the first model parameter by using the loss amount and the statistical center expected value corresponding to the loss amount until the loss amount reaches a presetcondition.

Description

technical field [0001] The embodiments of this specification relate to the field of computer technology, and in particular to a risk identification-based model training method, device and electronic equipment. Background technique [0002] Machine learning is a sub-discipline of artificial intelligence. Its main research is to let machines learn from past experiences, model the uncertainty of data, and predict the future. Generally, machine learning methods include the following two categories: [0003] a. Supervised methods: When there is labeled data, supervised methods can usually achieve stronger generalization capabilities; [0004] b. Unsupervised method: No data labeling is required, and malicious attacks can be prevented through anomaly detection technology; [0005] In actual business scenarios, in more cases, only a small number of positive samples and a large number of unlabeled samples may be obtained, but there may still be a small number of positive samples i...

AI Technical Summary

Problems solved by technology

Although for the convenience of operation, unlabeled samples can be used as negative samples for training, but how to improve the accuracy of model discrimination in this case has become a question worth thinking about

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