A Multi-Output Gradient Boosting Tree Modeling Method for Survival Risk Analysis

Abstract

The invention provides a multi-output gradient boosting tree modeling method for survival risk analysis. The method includes: firstly, under the model algorithm framework of the optimal gradient boosting tree (XGBoost), construct a , the expression of the survival data of the survival prediction model of the transportation or industrial target industry; then define and calculate the loss function corresponding to the survival data; then define and calculate the first-order gradient and second-order gradient corresponding to the loss function; finally calculate The obtained loss function value and the first-order gradient and second-order gradient value of the loss function are simultaneously input into the XGBoos model algorithm framework, and automatically trained to generate the survival prediction model of the target industry. The modeling method of the invention can better represent the relationship between model covariates and risk prediction values; improve the predictive performance and generalization ability of the model; have better predictive performance and risk discrimination; and be applicable to a wide range of scenarios.

Description

technical field [0001] The invention relates to the fields of computer survival analysis and machine learning, in particular to a multi-output gradient boosting tree modeling method for survival risk analysis. Background technique [0002] Survival risk analysis has a wide range of applications in many fields, such as finance, insurance, medical care, transportation, industry, etc. Survival risk analysis (referred to as survival analysis) is mainly to study the probability of a specific event occurring at the observation time point, and then estimate the risk curve and survival curve over time. Different from ordinary classification and regression problems, the research goal of survival risk analysis is the probability of a specific event occurring at a certain time point, not just a target variable, which makes it different from general research classification and regression problems. very different. Traditional survival risk analysis methods usually take the individual r...

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Problems solved by technology

[0013] The present invention establishes an effective survival prediction model, improves the accuracy of the survival prediction model, improves the constraints caused by the assumption of the survival prediction model on the potential random process (that is, the risk function of the individual), and solves the problem based on Insufficient interpretability of survival prediction models based on deep learning methods in practical applications

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