Feature binning algorithm based on decision tree

A decision tree and binning technology, applied in computing, computer components, instruments, etc., can solve problems such as subjectivity, unstable effects, and lack of uniform applicability, so as to improve accuracy, eliminate interference, and improve quality effect

CN110533519APending Publication Date: 2019-12-03杭州排列科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2019-12-03

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Abstract

The invention discloses a feature binning algorithm based on a decision tree. The algorithm comprises the steps of modeling a data sample, combining a feature variable and a target variable, setting alimiting condition, binning the decision tree and generating a binning result. The equipment provided by the invention can apply a machine learning decision tree algorithm to generate an optimal binning result under a certain condition, and the finally generated binning result achieves the optimal representation in data significance and eliminates the interference of subjective consciousness of modeling personnel. The algorithm is embedded into establishment of a traditional score card model or other emerging machine learning, the quality of a traditional credit model in the financial industry can be obviously improved, the approval accuracy is improved, and more fraud overdue applications are refused.
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Description

technical field

[0001] The invention belongs to the technical field of personal credit risk assessment in financial scenarios, and in particular relates to a feature binning algorithm based on a decision tree. Background technique

[0002] The credit scoring model is based on various historical credit data of banks or Internet financial customers to obtain credit scores of different grades. According to the customer's credit score, the credit institution can determine whether to grant credit and credit by analyzing the possibility of customer repayment after the loan amounts and interest rates.

[0003] Traditionally, banks or financial institutions adopt manual approval methods, and make subjective approval judgments based on the personal experience of the approvers, making the approval decision easily influenced by subjective factors, resulting in inconsistent approval results, unable to quantify the risk level, and unable to achieve risk control. Hierarchical management,...

Examples

Embodiment Construction

[0024] The present invention will be further described below in conjunction with the examples.

[0025] The following examples are used to illustrate the present invention, but cannot be used to limit the protection scope of the present invention. The conditions in the embodiment can be further adjusted according to the specific conditions, and the simple improvement of the method of the present invention under the premise of the concept of the present invention belongs to the protection scope of the present invention.

[0026] see Figure 1-2 , a feature binning algorithm based on a decision tree, including the following steps:

[0027] S1. Combining the characteristic variables and the target variables for the modeling data samples;

[0028] S2. Set the restriction conditions in the decision tree binning algorithm, including conditions such as the maximum depth of the decision tree, the minimum number of samples of leaf nodes and the number of special samples. The decision...