Teradata Package for Python Function Reference | 20.00 - get_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
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- lifecycle
- latest
- Product Category
- Teradata Vantage
- teradataml.hyperparameter_tuner.optimizer.GridSearch.get_model = get_model(self, model_id)
- DESCRIPTION:
Function to get the model.
PARAMETERS:
model_id:
Required Argument.
Specifies the unique identifier for model.
Notes:
* Trained model results returned for model trainer functions.
* Executed function results returned for non-model trainer
functions.
Types: str
RETURNS:
Object of teradataml analytic functions.
Note:
* Attribute references remains same as that of the function
attributes.
RAISES:
TeradataMlException, 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.
>>> # Retrieve the trained model.
>>> optimizer_obj.get_model(model_id="SVM_1")
############ output_data Output ############
iterNum loss eta bias
0 3 2.265289 0.028868 0.0
1 5 2.254413 0.022361 0.0
2 6 2.249260 0.020412 0.0
3 7 2.244463 0.018898 0.0
4 9 2.235800 0.016667 0.0
5 10 2.231866 0.015811 0.0
6 8 2.239989 0.017678 0.0
7 4 2.259956 0.025000 0.0
8 2 2.271862 0.035355 0.0
9 1 2.280970 0.050000 0.0
############ result Output ############
predictor estimate value
attribute
-7 Alpha 0.50000 Elasticnet
-3 Number of Observations 31.00000 None
5 Population -0.32384 None
0 (Intercept) 0.00000 None
-17 OneClass SVM NaN FALSE
-16 Kernel NaN LINEAR
-1 Loss Function NaN EPSILON_INSENSITIVE
7 Latitude 0.00000 None
-9 Learning Rate (Initial) 0.05000 None
-14 Epsilon 0.10000 None