A classification model training system and its implementation method

Abstract

A classification model training system and a realisation method therefor. The realisation method comprises: S1, creating a front-end management and display interface (1) of a SPARK algorithm classification model training system, and defining a front-end interaction request interface (101) of the front-end management and display interface (1); S2, creating a back-end service data source system (2) of the SPARK algorithm classification model training system; S3, based on the front-end interaction request interface (101) of the front-end management and display interface (1), creating a back-end service control interface (3), and establishing a correlation between the back-end service control interface (3) and the front-end interaction request interface (101); and S4, creating a SPARK algorithm training and optimised classification model based internal service logic in the back-end service control interface (3). By means of the classification model training system, an operation flow of classification model training can be effectively simplified, so as to effectively reduce the labour intensity for a developer and improve the development efficiency.

Description

technical field [0001] The present invention relates to the technical field of information processing, and more specifically, to a classification model training system and an implementation method thereof. Background technique [0002] At present, machine learning using the machine learning library SPARK.MLlib of the SPARK algorithm has become a common machine learning method. In order to use SPARK.MLlib for classification algorithm model training conveniently and quickly, and because the classification algorithm belongs to supervised learning, a large number of labeled samples need to be prepared in advance, divided into training samples and test samples, and then SPARK.MLlib uses these labeled samples To train the classification algorithm model, it is necessary to continuously adjust the samples and model parameters to optimize the classification algorithm model. [0003] The commonly used method of optimizing the classification model requires manually adding training sam...

AI Technical Summary

Problems solved by technology

Manually adding training samples and optimizing model parameters requires developers to spend a lot of time and energy on data preparation and program operation, resulting in low development efficiency

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