Data processing method and device based on joint model

Abstract

The invention discloses a data processing method and device based on a joint model. The method comprises the following steps: dividing an obtained original sample into a training set and a test set; and determining a first training sample, a second training sample and a third training sample according to the training set, and fitting the first training sample, the second training sample and the third training sample to obtain a corresponding logistic regression model and a corresponding evaluation index, so that a target model combination can be determined according to different evaluation indexes. According to the design, the integration tree model is combined with the logistic regression model, so that the nonlinear capability of the linear model is improved, the operation load of a computer is reduced in some service scenes, and the service processing efficiency is ensured.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a data processing method and device based on a joint model. Background technique [0002] In various business scenarios, there are usually problems such as data identification and classification processing, so as to realize the prediction and evaluation of different businesses. Generally speaking, there are many kinds of machine learning models used in business processing. Taking the logistic regression model as an example, most common business scenarios use the logistic regression model for business prediction. However, since the logistic regression model is a linear model, in some business Scenarios may increase the computing load of the computer, and it is difficult to guarantee the efficiency of business processing. Contents of the invention [0003] In order to improve the above problems, the present invention provides a joint model-based data processing m...

AI Technical Summary

Problems solved by technology

Generally speaking, there are many kinds of machine learning models used in business processing. Taking the logistic regression model as an example, most common business scenarios use the logistic regression model for business prediction. However, since the logistic regression model is a linear model, in some business Scenarios may increase the computing load of the computer, and it is difficult to guarantee the efficiency of business processing

Images