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Score card model derivative label generation method and device, equipment and storage medium

A scorecard and label technology, applied in the computer field, can solve the problems of omission of key information and lack of modeling accuracy, and achieve the effect of improving accuracy

Pending Publication Date: 2020-03-13
彩讯科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In feature engineering, the traditional scorecard model only conducts rough univariate analysis and relies on ready-made labels to enter the model, which often leads to the omission of key information, resulting in a lack of modeling accuracy

Method used

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  • Score card model derivative label generation method and device, equipment and storage medium
  • Score card model derivative label generation method and device, equipment and storage medium
  • Score card model derivative label generation method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] figure 1 It is a schematic flowchart of a method for generating a derived label of a scorecard model provided by Embodiment 1 of the present invention. This embodiment is applicable to generating derived labels in feature engineering of a scorecard model. Such as figure 1 As shown, a method for generating a scorecard model derived label provided in Embodiment 1 of the present invention includes:

[0044] S110. Obtain sample data.

[0045] Specifically, sample data includes positive sample data and negative sample data, sample data that meets a certain condition is called sample data, and sample data that does not meet a certain condition is called negative sample data. For example, if the test score ≥ 60 points is considered a pass, then the sample data with test scores ≥ 60 points is called positive sample data, and the sample data with test scores < 60 points is called negative sample data.

[0046] A certain scorecard model includes a large number of positive and ...

Embodiment 2

[0059] figure 2 It is a schematic flowchart of a method for generating a scorecard model-derived label provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above-mentioned embodiment. Such as figure 2 As shown, a method for generating a scorecard model-derived label provided in Embodiment 2 of the present invention includes:

[0060] S210. Obtain sample data.

[0061] Specifically, sample data includes positive sample data and negative sample data, sample data that meets a certain condition is called sample data, and sample data that does not meet a certain condition is called negative sample data. For example, if the test score ≥ 60 points is considered a pass, then the sample data with test scores ≥ 60 points is called positive sample data, and the sample data with test scores < 60 points is called negative sample data.

[0062] A certain scorecard model includes a large number of positive and negative sample data, but in thi...

Embodiment 3

[0089] image 3 It is a schematic structural diagram of a scorecard model derived label generation device provided in Embodiment 3 of the present invention. This embodiment is applicable to generate derived labels in feature engineering of the scorecard model. The scorecard model-derived label generation device provided in this embodiment can implement the scorecard model-derived label generation method provided in any embodiment of the present invention, and has the corresponding functions and structures of the execution method. The content that is not similarly described in this embodiment can be Reference is made to the description of any method embodiment of the invention. Such as image 3 As shown, the scorecard model-derived label generation device provided by Embodiment 3 of the present invention includes: a sample data acquisition module 310, an analysis parameter acquisition module 320, a sorting parameter acquisition module 330, a sample-derived label generation mod...

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Abstract

The embodiment of the invention discloses a score card model derivative label generation method and device, equipment and a storage medium. The method comprises the steps of acquiring sample data; obtaining analysis parameters of the sample data through correlation analysis; obtaining sorting parameters of the analysis parameters through a preset rule; performing binning operation on the sample data according to the sorting parameters to generate a sample derivative labels; and generating a derivative label of the score card model according to the sample derivative label. According to the method of the invention, the score card model is not limited to univariate analysis of the label when feature engineering is carried out, the derivative label with strong directivity can be generated, anda large number of available label boxes are provided for the subsequent score card model, so that the accuracy of the score card model is improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a method, device, device and storage medium for generating a scorecard model derived label. Background technique [0002] The current mainstream big data modeling method for customer acquisition and risk control is the scorecard model. The scorecard model uses advanced data mining technology and statistical analysis methods to systematically analyze a large amount of data on user characteristics, mine the behavior patterns and characteristics contained in the data, capture the relationship between historical information and future behavior performance, and develop A predictive model is generated, and a score is used to evaluate a user's future performance. [0003] Before using the algorithm to calculate, it is necessary to analyze the data and adjust the data format into a form that conforms to the logic of the algorithm, that is, feature enginee...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06K9/62G06Q10/06
CPCG06Q10/06393G06F16/2465G06F18/214
Inventor 杨良志白琳汪志新周光辉张奇张卓
Owner 彩讯科技股份有限公司
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