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
Published
March 2024
Language
English (United States)
Last Update
2024-04-10
dita:id
TeradataPython_FxRef_Enterprise_2000
Product Category
Teradata Vantage
teradataml.hyperparameter_tuner.optimizer.RandomSearch.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