Teradata Package for Python Function Reference | 20.00 - predict - 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
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- Product Category
- Teradata Vantage
- teradataml.hyperparameter_tuner.optimizer.RandomSearch.predict = predict(self, **kwargs)
- DESCRIPTION:
Function uses model training function generated models from SQLE,
VAL and UAF features for predictions. Predictions are made using
the best trained model. Predict function is not supported for
non-model trainer function.
PARAMETERS:
kwargs:
Optional Argument.
Specifies the keyword arguments. Accepts all merge model
predict feature arguments required for the teradataml
analytic function predictions.
RETURNS:
Output teradataml DataFrames can be accessed using attribute
references, such as HPTObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
>>> # Create an instance of the search algorithm called "optimizer_obj"
>>> # by referring "__init__()" method.
>>> # Perform "fit()" method on the optimizer_obj to populate model records.
>>> # Perform prediction using "optimizer_obj".
>>> optimizer_obj.predict(newdata=test_data, **eval_params)
id prediction MedHouseVal
0 686 0.202843 1.578
1 2018 0.149868 0.578
2 1754 0.211870 1.651
3 670 0.192414 1.922
4 244 0.247545 1.117