XGBoost prediction method of intelligent parameter optimization module

Abstract

The invention discloses an XGBoost prediction method of an intelligent parameter optimization module. The XGBoost prediction method belongs to the technical field of model parameter optimization and machine learning prediction, and comprises the steps of selecting a parameter group used by an XGBoost model, constructing an XGBoost model parameter group optimization module based on a genetic method, selecting a data sample set, taking 90% of samples in the data sample set as a training set, learning, training and verifying a proposed XGBoost prediction model with a genetic optimization parameter module by adopting the sample set, and comparing results. The XGBoost prediction method has the advantages that the problem that an appropriate parameter group cannot be found by a prediction modelof a data set of a large sample is solved, and a better prediction result can be obtained compared with an XGBoost model for adjusting parameters based on experience. The classification prediction result for a liver disease data set shows the effectiveness of the XGBoost prediction method.

Description

technical field [0001] The invention belongs to the technical field of model parameter optimization and machine learning prediction, and in particular provides an XGBoost (eXtreme Gradient Boosting, XGBoost for short) prediction method with an intelligent parameter optimization module, which is suitable for solving large samples, multiple features and problems in machine learning. There may be classification and regression prediction problems with missing features, which can be applied to data analysis and prediction, fault diagnosis, and financial data prediction and other fields. Background technique [0002] In recent years, artificial intelligence has achieved unprecedented development. Various intelligent methods such as machine learning, deep learning, and neural networks have been applied to many industries, improving enterprise efficiency and facilitating people's lives. However, as the amount of data increases and users' requirements for data prediction accuracy con...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a XGBoost parameter optimization module based on genetic algorithm, which adds an intelligent module with adjustable parameters to the existing XGBoost prediction model, which can greatly improve the prediction accuracy of XGBoost, and solve the problem of large samples, The problem of low prediction accuracy caused by the prediction model of a data set with multiple attributes and missing individual attributes cannot find a suitable parameter group, providing an intelligent prediction method with clearer usage scenarios and better prediction performance

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