Credit evaluation method based on fusion model, electronic device and storage medium

Abstract

The present invention discloses a credit evaluation method based on a fusion model. The method comprises: collecting individual personal credit data as a sample and simultaneously marking the credit rating; dividing the credit data into a plurality of training sets of equal element numbers through random sampling, and putting the training sets into different single classifiers, wherein each singleclassifier implements a classification algorithm; fusing results generated by each single classifier by using a fusion algorithm, extracting an optimal classification scheme, and recording the schemeby using a mathematical model to generate a preliminary model; and finally re-inputting the data to the preliminary model and verifying the data. The present invention also discloses an electronic device and a computer-readable storage medium applying to the method. According to the technical scheme of the present invention, multiple single classifiers are integrated to select the most appropriate classification scheme by means of an ensemble learning method, and respective weaknesses are overcome to exerting the greatest effect, so that the accuracy of the credit rating evaluation by using the fusion model can be improved.

Description

technical field [0001] The invention relates to the field of credit evaluation, in particular to a credit evaluation method, electronic device and storage medium based on a fusion model. Background technique [0002] With the gradual development of credit services, the importance of credit assessment technology is increasing day by day. The credit evaluation problem is essentially a classification problem, and the classifier is trained by the labeled training data to obtain the evaluation model. The k-NN algorithm, SVM algorithm and GBDT algorithm are algorithms for classifying data and are often used in fields involving credit evaluation. [0003] 1. The basic idea of ​​k-NN algorithm [0004] The k-NN algorithm, also known as the k-nearest neighbor method, the basic idea of ​​​​the k-NN method is: suppose that there are c categories w 1 ,w 2 ,w 3 ,…,w c The sample set of , each class has a sample N marked with the class i , i=1,2,...,c. [0005] Assuming that there...

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Problems solved by technology

[0020] In order to solve the above problems, one of the purposes of the present invention is to provide a credit evaluation method based on a fusion model, which can solve the problem that the traditional credit evaluation method is not accurate enough

[0021] The second object of the present invention is to provide an electronic device, which can solve the problem that the traditional credit evaluation method is not accurate enough

[0022] The third object of the present invention is to provide a computer-readable storage medium, which can solve the problem of inaccurate traditional credit evaluation methods

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