Credit evaluation model based on breadth learning

Abstract

The invention provides a credit evaluation model based on breadth learning. The credit evaluation model comprises the steps of obtaining credit data of natural persons in N source domains; performingdimension reduction processing and feature extraction on the credit data to obtain processed data; and constructing an initial model, and training the initial model according to the processed data toobtain a trained credit evaluation model, thereby solving the problems of single evaluation data and difficult model popularization of an existing evaluation model.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a credit evaluation model based on breadth learning. Background technique [0002] In recent years, with the rapid development of big data and artificial intelligence industries, data analysis and machine learning have been applied to all aspects of people's lives, but some new challenges have followed. For big data analysis, there is insufficient amount of required data, but data overflow with low or no correlation; most models can only meet a single type of data processing and analysis, and cannot handle the complex situation of multi-source domain data entanglement. For general machine learning, there are related learning problems such as training that takes a lot of time but can only be applied to a single scene; data that is too single leads to overfitting. [0003] Existing credit evaluation models are generally only applied to bank credit loans, mainly to evaluate perso...

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Problems solved by technology

[0006] In order to solve the above problems in the prior art, the present invention provides a credit evaluation model based on breadth learning, which can solve the problem that the existing evaluation model has single evaluation data and the model is difficult to promote

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