SG11201809890PA - Method of training random forest model, electronic device and storage medium - Google Patents

Method of training random forest model, electronic device and storage medium

Info

Publication number
SG11201809890PA
SG11201809890PA SG11201809890PA SG11201809890PA SG11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA
Authority
SG
Singapore
Prior art keywords
training
random forest
forest model
model
electronic device
Prior art date
Application number
SG11201809890PA
Inventor
Ge Jin
Liang Xu
Jing Xiao
Original Assignee
Ping An Technology Shenzhen Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ping An Technology Shenzhen Co Ltd filed Critical Ping An Technology Shenzhen Co Ltd
Publication of SG11201809890PA publication Critical patent/SG11201809890PA/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management

Abstract

METHOD OF TRAINING RANDOM FOREST MODEL, ELECTRONIC DEVICE AND STORAGE MEDIUM The disclosure relates to a method of training a random forest model, an electronic device and a storage medium. The method of training the random forest model includes: analyzing, by a system of controlling model training, whether model training conditions are met or not; if the model training conditions are met, determining whether reconstructive training needs to be carried out on the random forest model or not; if the reconstructive training needs to be carried out on the random forest model, carrying out the reconstructive training on the random forest model by using sample data; if the reconstructive training does not need to be carried out on the random forest model, carrying out corrective training on the random forest model by using the sample data. The disclosure can reduce the number of times of training on the random forest model, relieve the system burden and improve the system efficiency. Fig. 2
SG11201809890PA 2017-03-13 2017-06-30 Method of training random forest model, electronic device and storage medium SG11201809890PA (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710147698.3A CN107632995B (en) 2017-03-13 2017-03-13 The method and model training control system of Random Forest model training
PCT/CN2017/091362 WO2018166113A1 (en) 2017-03-13 2017-06-30 Random forest model training method, electronic apparatus and storage medium

Publications (1)

Publication Number Publication Date
SG11201809890PA true SG11201809890PA (en) 2018-12-28

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Family Applications (1)

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SG11201809890PA SG11201809890PA (en) 2017-03-13 2017-06-30 Method of training random forest model, electronic device and storage medium

Country Status (8)

Country Link
US (1) US20210081847A1 (en)
EP (1) EP3413212A4 (en)
JP (1) JP6587330B2 (en)
KR (1) KR102201919B1 (en)
CN (1) CN107632995B (en)
AU (1) AU2017404119A1 (en)
SG (1) SG11201809890PA (en)
WO (1) WO2018166113A1 (en)

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Also Published As

Publication number Publication date
KR102201919B1 (en) 2021-01-12
CN107632995B (en) 2018-09-11
AU2017404119A1 (en) 2018-10-11
JP2019513246A (en) 2019-05-23
EP3413212A1 (en) 2018-12-12
KR20190022431A (en) 2019-03-06
CN107632995A (en) 2018-01-26
EP3413212A4 (en) 2019-04-03
AU2017404119A9 (en) 2019-06-06
WO2018166113A1 (en) 2018-09-20
US20210081847A1 (en) 2021-03-18
JP6587330B2 (en) 2019-10-09

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