CREATE MODEL

Function

CREATE MODEL trains a machine learning model and saves the model.

Precautions

  • The model name must be unique. Pay attention to the naming format.
  • The AI training duration fluctuates greatly, and in some cases, the training duration is long. If the duration specified by the GUC parameter statement_timeout is too long, the training will be interrupted. You are advised to set statement_timeout to 0 so that the statement execution duration is not limited.

NOTE: In the Lite scenario, openGauss provides this syntax, but the AI capabilities are unavailable.

Syntax

CREATE MODEL model_name USING algorithm_name 
[FEATURES { {expression [ [ AS ] output_name ]} [, ...] }]
[TARGET { {expression [ [ AS ] output_name ]} [, ...] }]
FROM { table_name | select_query }
WITH hyperparameter_name = { hyperparameter_value | DEFAULT } [, ...] }

Parameter Description

  • model_name

    Name of the training model, which must be unique.

    Value range: a string. It must comply with the identifier naming convention.

  • architecture_name

    Algorithm type of the training model.

    Value range: a string. Currently, the value can be logistic_regression, linear_regression, svm_classification, or kmeans.

  • attribute_list

    Enumerated input column name of the training model.

    Value range: a string. It must comply with the naming convention of data attributes.

  • attribute_name

    Target column name of the retraining model in a supervised learning task (simple expression processing can be performed).

    Value range: a string. It must comply with the naming convention of data attributes.

  • subquery

    Data source.

    Value range: a string. It must comply with the SQL syntax of databases.

  • hyper_parameter_name

    Hyperparameter name of the machine learning model.

    Value range: a string. The value range varies according to the algorithm.

  • hp_value

    Hyperparameter value.

    Value range: a string. The value range varies according to the algorithm.

Examples

CREATE MODEL price_model USING logistic_regression
 FEATURES size, lot
 TARGET price
 FROM HOUSES
 (WITH learning_rate=0.88, max_iterations=default);

Helpful Links

DROP MODEL and PREDICT BY

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    openGauss 2024-04-12 00:46:38
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