CREATE SNOWFLAKE.ML.CLASSIFICATION¶

Creates a new classification model or replaces an existing model in the current or specified schema.

See also:: DROP SNOWFLAKE.ML.CLASSIFICATION

Syntax¶

CREATE [ OR REPLACE ] SNOWFLAKE.ML.CLASSIFICATION [ IF NOT EXISTS ] <model_name> (
    INPUT_DATA => <input_data>,
    TARGET_COLNAME => '<target_colname>',
    [CONFIG_OBJECT => <config_object>],
)
[ [ WITH ] TAG ( <tag_name> = '<tag_value>' [ , <tag_name> = '<tag_value>' , ... ] ) ]
[ COMMENT = '<string_literal>' ]

Copy

Parameters¶

Required

input_data

A reference to the training data. Using a reference allows the training process, which runs with limited privileges, to use your active role’s privileges to access the data. You can use a reference to a table or a view if your data is already in that form, or you can use a query reference to provide the query to be executed to obtain the data.

INPUT_DATA must contain the entire training data to be consumed by the classification model. Any columns that are not named in the TARGET_COLNAME arguments are considered training variables (features). The order of the columns in the input data is not important.

Feature columns must be STRING, NUMERIC, or BOOLEAN. STRING and BOOLEAN columns are treated as categorical features, while NUMERIC columns are considered continuous features. To treat a numeric column as categorical, cast it to STRING.

target_colname

Name of the column containing the label (target value) for each row in the training data. The target column may be BOOLEAN, NUMERIC, or STRING.

Optional

config_object

An OBJECT whose key-value pairs specify additional training options.

Key	Type	Default	Description
evaluate	BOOLEAN	TRUE	Whether evaluation metrics should be generated. If TRUE, then additional model is trained for evaluation using the parameters in the `evaluation_config`.
on_error	STRING	‘ABORT’	String constant that specifies the error handling method for the model training task. Supported values are: `'ABORT'`: Abort the entire training operation if any row results in an error. `'SKIP'`: Skip rows that result in an error. The error is shown instead of the results.
evaluation_config	OBJECT	NULL	A optional configuration object to specify how out-of-sample evaluation metrics should be generated. Currently, there is only one such option. `test_fraction` (FLOAT): The fraction of the dataset that should be used as test (evaluation) data. If evaluation configuration is not specified, the default behavior is to try to include a minimum of 500 instances of the minority class in the evaluation set and to limit the total test fraction of 20% of the dataset. This approach maintains balance in model evaluation and training, particularly for minority classes.

Key

Type

Default

Description

evaluate

BOOLEAN

TRUE

Whether evaluation metrics should be generated. If TRUE, then additional model is trained for evaluation using the parameters in the evaluation_config.

on_error

STRING

‘ABORT’

String constant that specifies the error handling method for the model training task. Supported values are:

'ABORT': Abort the entire training operation if any row results in an error.
'SKIP': Skip rows that result in an error. The error is shown instead of the results.

evaluation_config

OBJECT

NULL

A optional configuration object to specify how out-of-sample evaluation metrics should be generated. Currently, there is only one such option.

test_fraction (FLOAT): The fraction of the dataset that should be used as test (evaluation) data.

If evaluation configuration is not specified, the default behavior is to try to include a minimum of 500 instances of the minority class in the evaluation set and to limit the total test fraction of 20% of the dataset. This approach maintains balance in model evaluation and training, particularly for minority classes.

Access control requirements¶

A role used to execute this SQL command must have the following privileges at a minimum:

Privilege / Role	Object	Notes
CREATE SNOWFLAKE.ML.CLASSIFICATION	Schema	The role used to create a budget must be granted this privilege on the schema in which the budget is created.
OWNERSHIP	Schema	A role must be granted or inherit the OWNERSHIP privilege on the object to create a temporary object that has the same name as the object that already exists in the schema.
`model_name`!mladmin	SNOWFLAKE.ML.CLASSIFICATION instance	This role, scoped to the model itself, is initially granted to the owner, who can grant it to others to allow them to call all of the model’s methods. See Model Roles and Usage Privileges.
`model_name`!mlconsumer	SNOWFLAKE.ML.CLASSIFICATION instance	This role, scoped to the model itself, is initially granted to the owner, who can grant it to others to allow them to call the model’s prediction methods (such as `PREDICT`). See Model Roles and Usage Privileges.

Note that operating on any object in a schema also requires the USAGE privilege on the parent database and schema.

For instructions on creating a custom role with a specified set of privileges, see Creating custom roles.

For general information about roles and privilege grants for performing SQL actions on securable objects, see Overview of Access Control.

Example¶

See Examples.