A data binning method, device, computer equipment and storage medium
By employing automatic binning methods and distributed computing, the problem of low efficiency in variable binning in machine learning modeling is solved, enabling efficient selection of the optimal variable binning results and improving modeling effectiveness.
CN116680610BActive Publication Date: 2026-07-03NANJING NEBULA DIGITAL TECH CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING NEBULA DIGITAL TECH CO LTD
- Filing Date
- 2023-05-31
- Publication Date
- 2026-07-03
AI Technical Summary
Technical Problem
In machine learning modeling, due to limitations in human, material, and time resources, existing binning methods cannot effectively select variables that meet business requirements, resulting in poor modeling performance.
Method used
The automatic binning method categorizes variables into discrete and continuous variables based on their data type. It employs various binning methods and parameter ranges for automatic binning, and combines multiple linear regression and distributed computing to select the optimal variable binning result.
Benefits of technology
This reduced the computational burden on the system, avoided omissions in manual screening, improved the efficiency and accuracy of variable binning, and obtained the optimal variable binning results.
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Smart Images
Figure CN116680610B_ABST
Abstract
The application relates to a data binning method, device, computer equipment and storage medium. The method comprises the following steps: acquiring business data; dividing the business data into discrete variables and continuous variables; automatically binning each continuous variable to obtain a plurality of variable binning results corresponding to the continuous variable; in response to the number of categories of the discrete variables being less than or equal to a minimum category number threshold, outputting the discrete variables; in response to there being at least one discrete variable with a category number greater than the minimum category number threshold, automatically binning each discrete variable to obtain a plurality of variable binning results corresponding to the discrete variable; according to a preset evaluation index, screening the plurality of variable binning results corresponding to each continuous variable and / or discrete variable to obtain an optimal variable binning result; and outputting the optimal variable binning result and a parameter range of a corresponding binning method. The method can reduce the system calculation pressure, and can comprehensively obtain a plurality of variable binning results of data and an optimal variable binning result.
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