Example 5 Setup: Early Stopping Methods in Hyperparameter Tunning | teradataml - Example 5 Setup - Teradata Package for Python

Teradata® Package for Python User Guide

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
2024-04-09
dita:mapPath
nvi1706202040305.ditamap
dita:ditavalPath
plt1683835213376.ditaval
dita:id
rkb1531260709148
Product Category
Teradata Vantage

The following setup steps apply to all examples for Example 5: Early Stopping Methods in Hyperparameter Tunning.

Example Setup

  • Load example dataset iris data from teradataml.
    >>> load_example_data('teradataml','iris_input')
  • Create teradataml DataFrame.
    >>> df = DataFrame.from_table('iris_input')
  • Scale "target_columns" with respect to 'Range' value of the column.
    >>> scale_fit = ScaleFit(data=df,
                             target_columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
                             scale_method="Range")
  • Transform the data.
    >>> scale_transform = ScaleTransform(data=df,
                                         object=scale_fit.output,
                                         accumulate=["id", "species"])
  • Create data for training.
    >>> data = scale_transform.result