Method and apparatus for generating information

An apparatus for generating information, comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• receiving a modeling request, the modeling request comprising training data, a model type, a target number, and an evaluation indicator;
• determining a target number of initial machine learning pipelines according a type of the training data and the model type, the initial machine learning pipelines being used for model training;
• and
• executing following model generation steps using the target number of initial machine learning pipelines:
• generating the target number of new machine learning pipelines based on the target number of initial machine learning pipelines;
• performing model training based on the training data, the target number of initial machine learning pipelines, and the target number of new machine learning pipelines, to generate trained models;
• evaluating the obtained trained models respectively according to the evaluation indicator;
• determining whether a preset training termination condition is reached;
• and determining, in response to determining that the preset training termination condition is reached, a target trained model from the obtained trained models according to evaluation results.

A non-transitory computer readable medium storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform operations, the operations comprising:

• receiving a modeling request, the modeling request comprising training data, a model type, a target number, and an evaluation indicator;
• determining a target number of initial machine learning pipelines according a type of the training data and the model type, the initial machine learning pipelines being used for model training;
• and
• executing following model generation steps using the target number of initial machine learning pipelines:
• generating the target number of new machine learning pipelines based on the target number of initial machine learning pipelines;
• performing model training based on the training data, the target number of initial machine learning pipelines, and the target number of new machine learning pipelines, to generate trained models;
• evaluating the obtained trained models respectively according to the evaluation indicator;
• determining whether a preset training termination condition is reached;
• and determining, in response to determining that the preset training termination condition is reached, a target trained model from the obtained trained models according to evaluation results.