Model training method and apparatus thereof, storage medium and electronic equipment

Abstract

The invention discloses a model training method and an apparatus thereof. The method comprises the following steps of constructing a decision function model and training the decision function model so as to acquire an importance measurement value of a plurality of training samples; according to each importance measurement value, carrying out descending sort on each training sample, and making the sorted training samples generate a characteristic subspace; classifying each training sample in the characteristic subspace and acquiring a plurality of classification results, weighting and integrating the plurality of classification results so as to acquire a predicted score; and determining whether the predicted score is the same with a preset score, and when the predicted score is not the same with the preset score, adjusting a parameter of the decision function model. By using the method, accuracy of the predicted score is increased.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular, to a model training method, a model training device, a computer-readable storage medium, and electronic equipment. Background technique [0002] In recent years, consumer credit has developed rapidly, and the scale of various loan obligations such as housing, car purchases, credit cards, and student loans has continued to expand. The growing credit scale has brought great challenges to banks' risk assessment capabilities. In order to control risks, banks have to adopt strict credit review procedures, resulting in too complicated loan procedures and increasing transaction costs for consumer credit; in order to solve the above problems, it is particularly important to establish a sound personal credit system, and personal credit scoring is the system important technical links. [0003] At present, a variety of statistical models have been applied to personal cre...

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Problems solved by technology

In order to control risks, banks have to adopt strict credit review procedures, resulting in too complicated loan procedures and increasing transaction costs for consumer credit; in order to solve the above problems, it is particularly important to establish a sound personal credit system, and personal credit scoring is the system important technical links in

[0003] At present, a variety of statistical models have been applied to personal credit scoring, such as Logistic regression, neural network technology, support vector machines and other methods, which have been applied to credit scoring modeling to varying degrees, but the problem with a single model is that the classification accuracy is not high enough, and Not robust enough in the face of changes in credit data

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