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Methods defined here:
- __init__(self, object=None, newdata=None, id_column=None, detailed=False, terms=None, newdata_order_column=None, object_order_column=None)
- DESCRIPTION:
The DecisionForestPredict function uses the model generated by
the DecisionForest function to generate predictions on a response
variable for a test set of data. The model can be stored in either
a teradataml DataFrame or a DecisionForest object.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame containing the model data
or instance of DecisionForest.
object_order_column:
Optional Argument.
Specifies Order By columns for object.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
newdata:
Required Argument.
Specifies the teradataml DataFrame containing the input test data.
newdata_order_column:
Optional Argument.
Specifies Order By columns for newdata.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
id_column:
Required Argument.
Specifies a column containing a unique identifier for each test point
in the test set.
Types: str
detailed:
Optional Argument.
Specifies whether to output detailed information about the forest
trees; that is, the decision tree and the specific tree information,
including task index and tree index for each tree.
Default Value: False
Types: bool
terms:
Optional Argument.
Specifies the names of the newdata columns to copy to the output
teradataml DataFrame.
Types: str OR list of Strings (str)
RETURNS:
Instance of DecisionForestPredict.
Output teradataml DataFrames can be accessed using attribute
references, such as DecisionForestPredictObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example
load_example_data("decisionforestpredict", ["housing_train","housing_test"])
# Create teradataml DataFrame objects.
housing_test = DataFrame.from_table("housing_test")
housing_train = DataFrame.from_table("housing_train")
# Example 1 -
# First train the data, i.e., create a decision forest Model
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
)
# Run predict on the output of decision forest
decision_forest_predict_out = DecisionForestPredict(object = rft_model,
newdata = housing_test,
id_column = "sn",
detailed = False,
terms = ["homestyle"]
)
# Print the results
print(decision_forest_predict_out.result)
- __repr__(self)
- Returns the string representation for a DecisionForestPredict class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
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