Early stopping methods in hyperparameter tunning | RandomSearch | teradataml - Example 5: Early stopping methods in hyperparameter tunning - 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
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Product Category
Teradata Vantage
teradataml RandomSearch provides the capability of early stopping hyperparameter tuning based on the following:
  • Time based: Hyperparameter tuning is stopped once the maximum time is reached, thereby ceasing the optimization of hyperparameters.
  • Metrics based: Hyperparameter tuning is terminated once a trained model satisfies the specified minimum or maximum thresholds for the respective performance metrics, as dictated by the evaluation criteria.
    Metrics based method cannot be used for Non-Model Trainer function.
Both time and metrics methods can be used simultaneously, and hyperparameter tuning stops when either of these two methods satisfies the stopping condition.