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ContinuousParameterRange

Source code in docs_src/capo_forecast/types/continuous_parameter_range.py 
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class ContinuousParameterRange(TypedDict, closed=True):
    name: "capo_forecast.types.name.Name"
    """<p>The name of the hyperparameter to tune.</p>"""
    max_value: "capo_forecast.types.double.Double"
    """<p>The maximum tunable value of the hyperparameter.</p>"""
    min_value: "capo_forecast.types.double.Double"
    """<p>The minimum tunable value of the hyperparameter.</p>"""
    scaling_type: NotRequired["capo_forecast.types.scaling_type.ScalingType"]
    """<p>The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values:</p> <dl> <dt>Auto</dt> <dd> <p>Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter.</p> </dd> <dt>Linear</dt> <dd> <p>Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.</p> </dd> <dt>Logarithmic</dt> <dd> <p>Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.</p> <p>Logarithmic scaling works only for ranges that have values greater than 0.</p> </dd> <dt>ReverseLogarithmic</dt> <dd> <p>hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.</p> <p>Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0.</p> </dd> </dl> <p>For information about choosing a hyperparameter scale, see <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-ranges.html#scaling-type">Hyperparameter Scaling</a>. One of the following values:</p>"""

max_value instance-attribute 

max_value: Double

The maximum tunable value of the hyperparameter.

min_value instance-attribute 

min_value: Double

The minimum tunable value of the hyperparameter.

name instance-attribute 

name: Name

The name of the hyperparameter to tune.

scaling_type instance-attribute 

scaling_type: NotRequired[ScalingType]

The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values:

Auto

Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have values greater than 0.

ReverseLogarithmic

hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0.

For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values: