Method and apparatus for generating machine learning model

An apparatus for generating a machine learning model, the apparatus comprising:

• a generation unit, configured to generate model parameter combinations, and generate machine learning models respectively corresponding to the model parameter combinations, wherein the model parameters indicate an associated relationship between input vectors and output vectors of the machine learning models;
• a division unit, configured to execute a dividing operation:
• dividing preset machine learning data into training data and validation data;
• a processing unit, configured to execute training and validation operations:
• training the machine learning models in parallel respectively based on the training data;
• validating a learning accuracy of the trained machine learning models respectively based on the validation data to obtain validation scores, wherein the validation scores indicate a ratio of consistency between data types corresponding to the output vectors output by the machine leaning models based on the validation data and types of the validation data;
• and
• an execution unit, configured to execute a model generation operation:
• determining an optimal model parameter combination corresponding to a machine learning model to be generated based on the validation scores, and generating a machine learning model corresponding to the optimal model parameter combination.

An apparatus, comprising:

• a processor;
• and
• a memory,
• wherein the memory stores computer readable instructions capable being executed by the processor, the processor executes a method for generating a machine learning model when the computer readable instructions are executed, and the method comprises:
• generating model parameter combinations, and generating machine learning models respectively corresponding to the model parameter combinations, wherein the model parameters indicate an associated relationship between input vectors and output vectors of the machine learning models;
• executing a dividing operation:
• dividing preset machine learning data into training data and validation data;
• executing training and validation operations:
• training the machine learning models in parallel respectively based on the training data;
• validating a learning accuracy of the trained machine learning models respectively based on the validation data to obtain validation scores, wherein the validation scores indicate a ratio of consistency between data types corresponding to the output vectors output by the machine leaning models based on the validation data and types of the validation data;
• and
• executing a model generation operation:
• determining an optimal model parameter combination corresponding to a machine learning model to be generated based on the validation scores, and generating a machine learning model corresponding to the optimal model parameter combination.

A non-volatile computer storage medium storing computer readable instructions that may be executed by a processor, wherein when the computer readable instructions are executed by the processor, the processor executes a method for generating a machine learning model, and the method comprises:

• generating model parameter combinations, and generating machine learning models respectively corresponding to the model parameter combinations, wherein the model parameters indicate an associated relationship between input vectors and output vectors of the machine learning models;
• executing a dividing operation:
• dividing preset machine learning data into training data and validation data;
• executing training and validation operations:
• training the machine learning models in parallel respectively based on the training data;
• validating a learning accuracy of the trained machine learning models respectively based on the validation data to obtain validation scores, wherein the validation scores indicate a ratio of consistency between data types corresponding to the output vectors output by the machine leaning models based on the validation data and types of the validation data;
• and
• executing a model generation operation:
• determining an optimal model parameter combination corresponding to a machine learning model to be generated based on the validation scores, and generating a machine learning model corresponding to the optimal model parameter combination.