Teradata Package for Python Function Reference | 20.00 - delete_model - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.

Teradata® Package for Python Function Reference - 20.00

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
March 2024
Language
English (United States)
Last Update
2024-04-10
dita:id
TeradataPython_FxRef_Enterprise_2000
Product Category
Teradata Vantage
teradataml.catalog.model_cataloging.delete_model = delete_model(name, delete_objects=False)
DESCRIPTION:
    Delete a model, and optionally delete the model objects.
    A model can be deleted only by the creator of the model.
 
PARAMETERS:
    name:
        Required Argument.
        Specifies the name of the model to be deleted.
        Types: str
 
    delete_objects:
        Optional Argument.
        Specifies whether to drop the model objects also.
        When True, the model objects related to the model are deleted/dropped.
        Types: bool
        Default Value: False
 
RETURNS:
    None.
 
RAISES:
    TeradataMlException, TypeError, ValueError
 
EXAMPLES:
    # Load the data to run the example
    load_example_data("decisionforest", ["housing_train"])
 
    # Create teradataml DataFrame objects.
    housing_train = DataFrame.from_table("housing_train")
 
    # The examples use home sales data to create a
    # classification tree that predicts home style, which can be input
    # to the DecisionForestPredict.
    formula = "homestyle ~ driveway + recroom + fullbase + gashw + airco + prefarea + price + lotsize + bedrooms + bathrms + stories + garagepl"
    rft_model = DecisionForest(data=housing_train,
                               formula = formula,
                               tree_type="classification",
                               ntree=50,
                               tree_size=100,
                               nodesize=1,
                               variance=0.0,
                               max_depth=12,
                               maxnum_categorical=20,
                               mtry=3,
                               mtry_seed=100,
                               seed=100
                               )
 
    # Let's save this generated model.
    save_model(model=rft_model, name="decision_forest_model", description="Decision Forest test")
 
    # Example 1 - Only delete model information from the Model Catalog.
    delete_model('decision_forest_model')
 
    # Example 2 - Delete model information from the Model Catalog and drop model objects as well.
    delete_model('decision_forest_model', True)