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Training method, calling method, equipment and storage medium of neural network model

A neural network model and training sample technology, applied in the computer field, can solve problems such as low accuracy of output results, low accuracy of logistic regression models, inability to understand model output results, etc., to ensure accuracy, interpretability, and accuracy. high effect

Active Publication Date: 2021-04-20
深圳索信达数据技术有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The logistic regression model is easy to use, easy to understand, and the model is highly interpretable, but the accuracy of the logistic regression model is low, and the accuracy of the output results is not high
Complex machine learning models have high precision and high accuracy of output results, but the interpretability of complex machine learning models is weak, people cannot understand the reasons for the output results of the model, and there is inexplicability

Method used

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  • Training method, calling method, equipment and storage medium of neural network model
  • Training method, calling method, equipment and storage medium of neural network model
  • Training method, calling method, equipment and storage medium of neural network model

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Embodiment Construction

[0028] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0029] The flow charts shown in the drawings are just illustrations, and do not necessarily include all contents and operations / steps, nor must they be performed in the order described. For example, some operations / steps can be decomposed, combined or partly combined, so the actual order of execution may be changed according to the actual situation.

[0030] It should be understood that the terms used in the specification of this application are for the purpose of de...

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Abstract

The present application discloses a training method, calling method, equipment and storage medium of a neural network model. The method includes: obtaining training sample data; inputting the training sample data into a preset neural network model multiple times, sequentially Each polynomial in the neural network model is trained, wherein the neural network model includes multiple polynomials, and the multiple polynomials include high-order polynomials; according to each of the polynomials after training, complete the neural network Model training. The neural network model takes into account the accuracy and interpretability of the model.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a neural network model training method, calling method, device and storage medium. Background technique [0002] In binary classification scenarios such as intelligent recommendation and intelligent risk control, models such as logistic regression model and complex machine learning model are usually used for binary classification processing, such as recommending or not recommending, passing or rejecting, etc. The logistic regression model is easy to use, easy to understand, and the model is highly interpretable, but the accuracy of the logistic regression model is low, and the accuracy of the output results is not high. Complex machine learning models have high precision and high accuracy of output results, but the interpretability of complex machine learning models is weak, people cannot understand the reasons for the output results of the model, and there is ine...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/04G06N3/08G06K9/62G06F16/9535
CPCG06N3/08G06F16/9535G06N3/047G06N3/045G06F18/24323
Inventor 邵俊张磊曹新建
Owner 深圳索信达数据技术有限公司