Model training method and related apparatus

A non-transitory computer-readable storage medium, wherein the computer-readable storage medium has program instructions, and when the program instructions are directly or indirectly executed by one or more processors, the one or more processors is to perform operations comprising:

• calculating a loss function of an error imputation model based on a first error of a prediction result of a prediction model for first sample data, a first output of the error imputation model, and a probability that the first sample data is observed, wherein the first output of the error imputation model represents a predicted value of the first error, the loss function of the error imputation model comprises a bias term and a variance term, the bias term represents a bias between an actual loss function and a theoretical loss function of the prediction model, and the variance term represents a variance between the actual loss function and the theoretical loss function of the prediction model;
• and
• updating a parameter of the error imputation model based on the loss function of the error imputation model.