An extreme value gradient lifting logistic regression classification prediction method

Abstract

The invention relates to an extreme value gradient lifting logical regression classification prediction method, belonging to the field of big data analysis and intelligent classification prediction. After the extreme value gradient lifting model is used to learn the samples, each sample falls on each classification and the leaf node position of the regression tree for unique thermal coding to combine into a new feature, and then combines with the previous features to form a combination feature, so that the features of the samples are increased and new samples are formed. Logistic regression isused to classify and forecast the new samples. Fusion of Extreme Value Gradient Lifting and Logistic Regression; Using extreme value gradient lifting to select features, choosing cart tree as the base classifier, utilizing kini impurity to form a series of uncorrelated features, enlarging the dimension of features, and training new features into logistic regression model will have a better prediction effect. The invention has the advantages that the feature selection and the feature expansion functions of the extreme value gradient lifting are respectively utilized, and the problem of low prediction accuracy of a single model logical regression model is solved.

Description

technical field [0001] The invention belongs to the field of big data analysis and intelligent classification prediction, and provides an extreme value gradient lifting logistic regression classification prediction method, which is suitable for solving continuous or discrete variables, classification and prediction of multi-sample multi-dimensional discrete or continuous feature data, and can be applied In the fields of medical diagnosis, fault diagnosis and accuracy prediction. Background technique [0002] Data processing, analysis, and feature classification prediction are widely used in various fields. With the vigorous development of artificial intelligence and machine learning theories and methods, classification prediction based on deep learning is widely used in speech systems, face recognition and target detection, and has achieved breakthrough progress. However, because the classification prediction method based on machine learning cannot satisfy the learning of l...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide an extreme value gradient boosting logistic regression classification prediction method to solve the problem of high-precision classification prediction of limited sample data with fewer features

The research of the present invention finds that the fusion of extreme value gradient lifting feature extraction method and logistic regression modeling is one of the effective ways to solve the high-precision classification prediction of limited sample data

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