Multi-output gradient lifting tree modeling method for survival risk analysis

Abstract

The invention provides a multi-output gradient lifting tree modeling method for survival risk analysis, which comprises the following steps: firstly, constructing an expression of survival data for establishing a survival prediction model of financial, insurance, medical, traffic or industrial target industries under a model algorithm framework of an optimal gradient lifting tree (XGBoost); then defining and calculating a loss function corresponding to the survival data; then, defining and calculating a first step degree and a second step degree corresponding to the loss function; and finally,inputting the calculated loss function value and the first-order gradient value and the second-order gradient value of the loss function into an XGBoos model algorithm framework at the same time, andperforming automatic training to generate a survival prediction model of the target industry. The modeling method provided by the invention can better represent the relationship between the model covariable and the risk prediction value. The prediction performance and the generalization capability of the model are improved. The prediction performance and the risk distinguishing degree are better,and the application scene is wide.

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. Big difference. 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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