teradataml.catalog.model_cataloging.describe_model = describe_model(name)

DESCRIPTION:
    List details of the model, if accessible to the user.
 
PARAMETERS:
    name:
        Required Argument.
        Specifies the name of the model to list the details for.
        Types: str
 
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")
 
    # This example uses 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")
 
    # List all details of recently saved model 'decision_forest_model'.
    describe_model(name="decision_forest_model")