Struct aws_sdk_forecast::model::HyperParameterTuningJobConfig

#[non_exhaustive]
pub struct HyperParameterTuningJobConfig { /* private fields */ }

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.

Implementations

impl HyperParameterTuningJobConfig

pub fn parameter_ranges(&self) -> Option<&ParameterRanges>

Specifies the ranges of valid values for the hyperparameters.

impl HyperParameterTuningJobConfig

pub fn builder() -> Builder

Creates a new builder-style object to manufacture HyperParameterTuningJobConfig.

Trait Implementations

impl Clone for HyperParameterTuningJobConfig

fn clone(&self) -> HyperParameterTuningJobConfig

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for HyperParameterTuningJobConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<HyperParameterTuningJobConfig> for HyperParameterTuningJobConfig

fn eq(&self, other: &HyperParameterTuningJobConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for HyperParameterTuningJobConfig