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User classification method and device based on random forest, equipment and storage medium

A technology of random forest and classification method, which is applied in data processing applications, computer parts, instruments, etc., can solve the problems of unfavorable users in finding the products they need, low user classification accuracy, and large amount of classification calculations, etc., to achieve Reduce the amount of fitting calculations, reduce the probability, and improve the effect of fitting speed

Pending Publication Date: 2022-01-11
PING AN BANK CO LTD
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AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present application provides a random forest-based user classification method, device, device, and storage medium to solve the problem that the user classification method in the prior art has a large amount of calculation for user classification and inaccurate classification. The system's user classification accuracy is not high, which is not conducive to improving the efficiency of users to find the products they need

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  • User classification method and device based on random forest, equipment and storage medium
  • User classification method and device based on random forest, equipment and storage medium
  • User classification method and device based on random forest, equipment and storage medium

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Embodiment Construction

[0043] In the following description, specific details such as specific system structures and techniques are proposed for explanation, such as specific details, such as specific system structures, and techniques. However, it will be apparent to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, a detailed description of well known systems, devices, circuits, and methods is omitted to prevent unnecessary details to prevent the presentation.

[0044] In order to illustrate the technical solution described herein, the following will be described below.

[0045] Random forest algorithm is a classifier that uses multiple trees to train and predict. The general process of the random forest algorithm is as follows:

[0046] 1. Determine the training sample (or may also be called training case). Determining the number of training samples is N, the sample characteristics of training samples (for ...

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Abstract

The invention belongs to the field of artificial intelligence, and provides a user classification method and device based on a random forest, equipment and a storage medium. The method comprises the following steps: acquiring a training sample set, wherein training samples in the training sample set comprise user features and user classifications; clustering the training samples according to user features in the training sample set, and determining a sample subset according to a clustering result; respectively constructing corresponding classification trees according to the determined sample subsets; and determining a random forest according to the classification tree, and classifying to-be-classified users according to the random forest. Through the recommendation method, the construction efficiency of the random forest can be effectively improved, the similarity problem of classification trees can be effectively reduced, generation of a sample subset missing training samples is avoided, and the classification precision of the random forest is improved, so that user classification can be more accurately performed, and the efficiency of obtaining required products by users is improved.

Description

Technical field [0001] The present application belongs to the field of manual intelligence, in particular, to user classification methods, devices, equipment, and storage media based on random forests. Background technique [0002] When users use financial platforms or products to purchase web pages, the products in the platform or page are more, and people faced by each product will be different. In order to enable the user to efficiently find the product that matches the user, the user's category is typically identified, and the corresponding product is recommended based on the identified category. [0003] The currently commonly used method includes forming a training set in a way with a random forest algorithm, and determines a plurality of decision trees based on the training set formed, and a random forest consisting of multiple decision trees. However, due to the formation of training sets in a random forest algorithm, it is possible to extract duplicate data, as well as d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/764G06V10/762G06V10/774G06Q30/06
CPCG06Q30/0631G06F18/23213G06F18/214G06F18/24323
Inventor 王洪波余涛杨贵锋
Owner PING AN BANK CO LTD
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