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Decision tree construction method and device based on column storage, equipment and storage medium

A construction method and decision tree technology, applied in the field of machine learning, can solve problems such as time-consuming and low work efficiency, and achieve the effect of improving work efficiency and reducing the time for data reading

Pending Publication Date: 2020-06-12
WEBANK (CHINA)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Constructing a decision tree in the case of a large amount of data will take a lot of time in the process of data reading, and the work efficiency is not high

Method used

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  • Decision tree construction method and device based on column storage, equipment and storage medium
  • Decision tree construction method and device based on column storage, equipment and storage medium
  • Decision tree construction method and device based on column storage, equipment and storage medium

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Embodiment Construction

[0038] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0039] The column storage-based decision tree construction device mainly involved in the embodiment of the present invention refers to a network connection device capable of realizing network connection, and the column storage-based decision tree construction device may be a server, a cloud platform, or the like.

[0040] refer to figure 1 , figure 1 It is a schematic diagram of the hardware structure of the column storage-based decision tree construction device involved in various embodiments of the present invention. In the embodiment of the present invention, the device for constructing a decision tree based on column storage may include a processor 1001 (such as a central processing unit, CPU), a communication bus 1002, an input port 1003, an output port 1004, and a memory 1005. Wherein, the communication bus 10...

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Abstract

The invention discloses a decision tree construction method and device based on column storage, equipment and a storage medium, and relates to the technical field of machine learning, and the method comprises the steps: reading corresponding column data according to a first splitting feature of each node in a current layer, and dividing each sample to each node in the current layer according to the column data; obtaining a grouping sample in each node, and counting the feature distribution condition of each feature in the grouping sample; and generating a next layer of nodes according to the feature distribution condition, and splitting the next layer of nodes until convergence to obtain a decision tree. Therefore, the decision tree is constructed based on column storage, the data readingtime is shortened, and the working efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a method, device, equipment and storage medium for constructing a decision tree based on column storage. Background technique [0002] With the development of computer technology, more and more technologies (big data, distributed, blockchain, artificial intelligence, etc.) The industry's security and real-time requirements also put forward higher requirements for technology. [0003] Decision trees are widely used in the financial field, but the current decision tree construction is mainly based on row storage for data reading. Constructing a decision tree in the case of a large amount of data will take a lot of time in the process of data reading, and the work efficiency will not be high. Contents of the invention [0004] The present invention provides a decision tree construction method, device, equipment and storage medium based on column storage, aiming ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/10G06K9/62G06Q40/02
CPCG06N20/10G06Q40/03G06F18/24323
Inventor 李诗琦黄启军黄铭毅刘玉德
Owner WEBANK (CHINA)