Backward model selection method and device and readable storage medium
A model selection and memory technology, applied in computing models, machine learning, computing, etc., can solve the problems of high modeling threshold and low efficiency
Pending Publication Date: 2020-05-29
WEBANK (CHINA)
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[0004] The main purpose of this application is to provide a backward model selection method, device and readable storage medium, aiming to solve the technical problems of high threshold and low efficiency in the backward selection mode modeling in the prior art
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[0082] 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.
[0083] The embodiment of the present application provides a backward model selection method, the backward model selection method is applied to the server, in the first embodiment of the backward model selection method of the present application, refer to figure 1 , the backward model selection method includes:
[0084] Step S10, receiving the configuration parameters sent by the client associated with the server and obtaining the features to be trained, and training the preset model to be trained based on each of the features to be trained and the configuration parameters to obtain the first initial training Model;
[0085] 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 for model ...
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The invention discloses a backward model selection method and device and a readable storage medium. The backward model selection method comprises the following steps: receiving configuration parameters sent by a client associated with the server and obtaining to-be-trained features, training a preset to-be-trained model based on the to-be-trained features and the configuration parameters; obtaining a first initial training model, calculating a first significance corresponding to each to-be-trained feature, based on each first significance, obtaining a second significance, rejecting to-be-rejected features meeting a preset rejection significance requirement from the to-be-trained features; and based on the configuration parameters, selecting a target training model from the first initial training model and the loop training model set, generating visual data corresponding to the target training model, and feeding back the visual data to the client. The technical problems of high modelingthreshold and low efficiency of the backward selection mode are solved.
Description
technical field [0001] The present application relates to the technical field of artificial intelligence of financial technology (Fintech), and in particular to a backward 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, the backward selection mode is an important model selection strategy. Compar...
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IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 唐兴兴黄启军陈瑞钦林冰垠李诗琦
Owner WEBANK (CHINA)