Struct aws_sdk_sagemaker::types::builders::AutoMlJobObjectiveBuilder

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

A builder for AutoMlJobObjective.

Implementations

impl AutoMlJobObjectiveBuilder

pub fn metric_name(self, input: AutoMlMetricEnum) -> Self

The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

For the list of all available metrics supported by Autopilot, see Autopilot metrics.

If you do not specify a metric explicitly, the default behavior is to automatically use:

• For tabular problem types:

  • Regression: MSE.

  • Binary classification: F1.

  • Multiclass classification: Accuracy.

• For image or text classification problem types: Accuracy

• For time-series forecasting problem types: AverageWeightedQuantileLoss

pub fn set_metric_name(self, input: Option<AutoMlMetricEnum>) -> Self

The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

For the list of all available metrics supported by Autopilot, see Autopilot metrics.

If you do not specify a metric explicitly, the default behavior is to automatically use:

• For tabular problem types:

  • Regression: MSE.

  • Binary classification: F1.

  • Multiclass classification: Accuracy.

• For image or text classification problem types: Accuracy

• For time-series forecasting problem types: AverageWeightedQuantileLoss

pub fn get_metric_name(&self) -> &Option<AutoMlMetricEnum>

The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

For the list of all available metrics supported by Autopilot, see Autopilot metrics.

If you do not specify a metric explicitly, the default behavior is to automatically use:

• For tabular problem types:

  • Regression: MSE.

  • Binary classification: F1.

  • Multiclass classification: Accuracy.

• For image or text classification problem types: Accuracy

• For time-series forecasting problem types: AverageWeightedQuantileLoss

pub fn build(self) -> AutoMlJobObjective

Consumes the builder and constructs a AutoMlJobObjective.

Trait Implementations

impl Clone for AutoMlJobObjectiveBuilder

fn clone(&self) -> AutoMlJobObjectiveBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlJobObjectiveBuilder

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

Formats the value using the given formatter.

impl Default for AutoMlJobObjectiveBuilder

fn default() -> AutoMlJobObjectiveBuilder

Returns the “default value” for a type.

impl PartialEq<AutoMlJobObjectiveBuilder> for AutoMlJobObjectiveBuilder

fn eq(&self, other: &AutoMlJobObjectiveBuilder) -> 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 AutoMlJobObjectiveBuilder