Step-by-step model selection method and device and readable storage medium
A model selection and model technology, applied in the field of machine learning of financial technology, can solve problems such as low efficiency and high modeling threshold
Pending Publication Date: 2020-06-05
WEBANK (CHINA)
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[0004] The main purpose of this application is to provide a step-by-step model selection method, device and readable storage medium, aiming to solve the technical problems of high threshold and low efficiency of step-by-step model selection in the prior art
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[0106] It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.
[0107] The embodiment of the present application provides a step-by-step model selection method, the step-by-step model selection method is applied to the server, in the first embodiment of the step-by-step model selection method of the present application, refer to figure 1 , the stepwise model selection method includes:
[0108] Step S10, receiving the configuration parameters sent by the client associated with the server and obtaining the feature set to be trained, and training the preset model to be trained based on the feature set to be trained and the configuration parameters to obtain the first initial training model;
[0109] In this embodiment, it should be noted that the client includes a visual interface, on which the user can configure the parameters of the preset model to be trained ...
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The invention discloses a step-by-step model selection method and device and a readable storage medium. The step-by-step model selection method comprises the following steps: selecting a step-by-stepmodel; receiving configuration parameters sent by a client associated with the server and obtaining a to-be-trained feature set, training a preset to-be-trained model based on the to-be-trained feature set and the configuration parameters; obtaining a first initial training model, respectively calculating a first type significance and a second type significance corresponding to the to-be-trained feature set; based on the first type significance and the second type significance, performing loop training on the first initial training model to obtain a loop training model set, selecting a targettraining model from the first initial training model and the loop training model set based on the configuration parameters, generating visual data corresponding to the target training model, and feeding back the visual data to the client. The technical problems of high modeling threshold and low efficiency of the step-by-step selection mode in the prior art are solved.
Description
technical field [0001] The present application relates to the technical field of machine learning in financial technology (Fintech), and in particular to a step-by-step model selection method, device and readable storage medium. Background technique [0002] With the continuous development of financial technology, especially Internet technology and finance, more and more technologies (such as distributed, blockchain, artificial intelligence, etc.) Requirements, such as the distribution of corresponding to-do items in the financial industry also have higher requirements. [0003] With the continuous development of computer software and artificial intelligence, the application of machine learning modeling is becoming more and more extensive. In existing technologies, scenarios such as financial risk control and medical models are usually modeled using logistic regression models. In model modeling, step-by-step selection mode is an important model selection strategy. Compared ...
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Login to View More IPC IPC(8): G06F30/27G06N20/00
CPCG06N20/00
Inventor 唐兴兴黄启军陈瑞钦林冰垠李诗琦
Owner WEBANK (CHINA)