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Class: Aws::ForecastService::Types::HyperParameterTuningJobConfig

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing HyperParameterTuningJobConfig as input to an Aws::Client method, you can use a vanilla Hash:

{
  parameter_ranges: {
    categorical_parameter_ranges: [
      {
        name: "Name", # required
        values: ["Value"], # required
      },
    ],
    continuous_parameter_ranges: [
      {
        name: "Name", # required
        max_value: 1.0, # required
        min_value: 1.0, # required
        scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
      },
    ],
    integer_parameter_ranges: [
      {
        name: "Name", # required
        max_value: 1, # required
        min_value: 1, # required
        scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
      },
    ],
  },
}

Configuration information for a hyperparameter tuning job. You specify this object in the CreatePredictor request.

A hyperparameter is a parameter that governs the model training process. You set hyperparameters before training starts, unlike model parameters, which are determined during training. The values of the hyperparameters effect which values are chosen for the model parameters.

In a hyperparameter tuning job, Amazon Forecast chooses the set of hyperparameter values that optimize a specified metric. Forecast accomplishes this by running many training jobs over a range of hyperparameter values. The optimum set of values depends on the algorithm, the training data, and the specified metric objective.

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#parameter_ranges ⇒ Types::ParameterRanges

Specifies the ranges of valid values for the hyperparameters.

Returns: