A taxpayer credit evaluation method based on distributed automatic feature combination

Abstract

The invention discloses a distributed automatic feature combination taxpayer credit evaluation method. The steps of the method include: 1) using the training samples to train the random forest model using the MapReduce distributed computing framework to obtain a distributed random forest model; 2) inputting the training samples into the distributed random forest model to generate each training input Multiple combined features of the sample; 3) Merge the generated combined features with the feature information of the corresponding taxpayer; 4) Use the combined features to train the scorecard model; 5) For a taxpayer to be evaluated for credit, use the distribution The random forest model generates the combined features of the taxpayer and merges them with the taxpayer's feature information, and then inputs the combined features of the taxpayer into the trained scorecard model to predict the taxpayer's credit score. The invention can carry out accurate credit evaluation of taxpayers.

Description

technical field [0001] The invention relates to a credit evaluation model and a taxpayer credit evaluation method, in particular to a credit evaluation model and a taxpayer credit evaluation method using distributed random forest for automatic feature combination, belonging to the field of computer big data processing. [0002] technical background [0003] Credit evaluation has been developed in the field of bank credit for decades. It is mainly used to evaluate the personal credit of people applying for loans, and to assist in the issuance of loans through credit evaluation to reduce the risk of bank capital gains and capital recovery. [0004] Taxpayer credit assessment in the field of taxation has only emerged in recent years, and it is mainly based on expert experience. Tax experts select indicators that can represent tax risks according to their professional experience, and assign different scores to different indicators. For each taxpayer, a lot of manual analysis and ...

AI Technical Summary

Problems solved by technology

However, the scorecard model can only process processed features. To obtain a more accurate credit scoring effect, a large number of professionals are required to construct carefully calculated indicators.

Images