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  • Description
  • Steps impacted
  • Example
  1. Pipelines
  2. Forecasting Pipelines
  3. Configuration
  4. Using Hyperparameter Tuning for the Model

hyperparameter_tuning_parameters

Configures hyper parameters tuning.

Description

This function defines the hyperparameter space that the model will explore during tuning. Specifying a range of possible values for each hyperparameter allows the model to test various combinations and determine which settings optimize performance.

Steps impacted

training

Example

hyperparameter_tuning_parameters:
  scorer: neg_mean_absolute_error
  scorer_args:
  cv: 5
  splitter: kfold
  engine: smac
  max_iter: 50
  n_jobs: 72
Previoustuning_search_paramsNextEvaluating the Results of the Forecast

Last updated 1 year ago