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Methods defined here:
- __init__(self, modeldata=None, newdata=None, id_col=None, responses=None, formula=None, newdata_order_column=None, modeldata_order_column=None)
- DESCRIPTION:
The NaiveBayesPredict function uses the model output generated by the
NaiveBayes function to predict the outcomes for a test set of data.
PARAMETERS:
modeldata:
Required Argument.
Specifies the teradataml DataFrame containing the model data
or instance of NaiveBayes.
modeldata_order_column:
Optional Argument.
Specifies Order By columns for modeldata.
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_col:
Required Argument.
Specifies the name of the column that contains the ID that uniquely
identifies the test input data.
Types: str
responses:
Required Argument.
Specifies a list of Responses to output.
Types: str OR list of Strings (str)
formula:
Optional Argument.
Required when the modeldata is a teradataml DataFrame.
A string consisting of "formula".
Specifies the model to be fitted. Only basic formula of the
"col1 ~ col2 + col3 +..." form is supported and all variables
must be from the same virtual data frame object. The response
should be column of type real, numeric, integer or boolean.
Types: str
RETURNS:
Instance of NaiveBayesPredict.
Output teradataml DataFrames can be accessed using attribute
references, such as NaiveBayesPredictObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example
load_example_data("NaiveBayesPredict",["nb_iris_input_test","nb_iris_input_train"])
# Create teradataml DataFrame objects.
nb_iris_input_train = DataFrame.from_table("nb_iris_input_train")
nb_iris_input_test = DataFrame.from_table("nb_iris_input_test")
# Example 1 -
# Run the train function
naivebayes_train = NaiveBayes(formula="species ~ petal_length + sepal_width + petal_width + sepal_length",
data=nb_iris_input_train)
# Generate prediction using output of train function
naivebayes_predict_result = NaiveBayesPredict(newdata=nb_iris_input_test,
modeldata = naivebayes_train,
id_col = "id",
responses = ["virginica","setosa","versicolor"]
)
# Print result dataframe
print(naivebayes_predict_result.result)
- __repr__(self)
- Returns the string representation for a NaiveBayesPredict class instance.
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