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Method, apparatus, device and storage medium for generating machine learning model

A machine learning model and model generation technology, applied in the field of machine learning, can solve problems such as limiting the widespread use of machine learning modeling tools, and achieve the effect of improving generalization and automation performance

Active Publication Date: 2019-03-01
SHENZHEN LEXIN SOFTWARE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, algorithms commonly used in the industry and with good effects are not supported, such as xgboost, lgbm or catboost, which also limits the widespread use of machine learning modeling tools to a certain extent.

Method used

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  • Method, apparatus, device and storage medium for generating machine learning model
  • Method, apparatus, device and storage medium for generating machine learning model
  • Method, apparatus, device and storage medium for generating machine learning model

Examples

Experimental program
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Embodiment 1

[0034] figure 1 It is a flow chart of a method for generating a machine learning model provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where a machine learning model and matching target display information are automatically generated. This method can be implemented by a machine learning model generation device To execute, the device can be implemented by software and / or hardware, and can generally be integrated into computer equipment. Correspondingly, such as figure 1 As shown, the method includes the following operations:

[0035] S110. Obtain model association parameters input by the user through the human-computer interaction interface, where the model association parameters include: model generation data and display information types.

[0036] Wherein, the model-associated parameters may be related parameters used to generate a machine learning model, such as model generation data and model generation algorithms. Model ...

Embodiment 2

[0055] Figure 2a It is a flow chart of a method for generating a machine learning model provided in Embodiment 2 of the present invention, Figure 2b It is a flow chart of a method for generating a machine learning model provided in Embodiment 2 of the present invention, Figure 2c It is a flow chart of a method for generating a machine learning model provided in Embodiment 2 of the present invention. This embodiment is embodied on the basis of the above-mentioned embodiments. In this embodiment, it is given to generate At least one machine learning model matched with at least one model generation algorithm, and according to the generated machine learning model, a specific implementation manner of target display information matching the display information type is generated.

[0056] Correspondingly, such as Figure 2a As shown, when the model association parameters include model generation data, model generation algorithm, and machine learning model file and model report f...

Embodiment 3

[0102] image 3 is a schematic diagram of a device for generating a machine learning model provided in Embodiment 3 of the present invention, such as image 3 As shown, the device includes: a model associated parameter acquisition module 310, a machine learning model generation module 320, a target presentation information generation module 330, and a target presentation information provision module 340, wherein:

[0103] The model associated parameter acquisition module 310 is used to acquire the model associated parameters input by the user through the human-computer interaction interface, and the model associated parameters include: model generation data and display information type;

[0104] A machine learning model generation module 320, configured to generate at least one machine learning model matching at least one model generation algorithm according to the model generation data and the display information type;

[0105] A target display information generating module ...

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Abstract

The embodiment of the invention discloses a method, an apparatus, a device and a storage medium for generating a machine learning model. The method comprises the following steps: acquiring a model association parameter input by a user through a human-computer interaction interface; the model association parameter comprises model generation data and display information types; generating at least one machine learning model matching at least one model generation algorithm according to the model generation data and the presentation information type; Generating target presentation information matching the presentation information type according to the generated machine learning model; providing the target presentation information to the user. The technical proposal of the embodiment of the invention can make the machine learning modeling process more generalized and automated.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of machine learning, and in particular, to a method, device, device, and storage medium for generating a machine learning model. Background technique [0002] Machine learning is a very important research field in computer science and artificial intelligence. Developing a machine learning model is a time-consuming, expert-driven workflow that includes data preparation, feature selection, model or technique selection, training, and tuning. . [0003] Existing open source automated machine learning modeling tools in the field of machine learning, such as Tpot, Auto-sklearn, etc., can simplify the modeling process by calling their packaged built-in functions, automatically search for hyperparameters, and achieve automatic or semi-automatic modeling the goal of. [0004] In the process of realizing the present invention, the inventor finds that the prior art has the following defects: ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N99/00
Inventor 钱信羽
Owner SHENZHEN LEXIN SOFTWARE TECH CO LTD
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