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.

The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

• For tabular problem types:

  • List of available metrics:

    • Regression: MAE, MSE, R2, RMSE

    • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

    For a description of each metric, see Autopilot metrics for classification and regression.

  • Default objective metrics:

    • Regression: MSE.

    • Binary classification: F1.

    • Multiclass classification: Accuracy.

• For image or text classification problem types:

  • List of available metrics: Accuracy

    For a description of each metric, see Autopilot metrics for text and image classification.

  • Default objective metrics: Accuracy

• For time-series forecasting problem types:

  • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE

    For a description of each metric, see Autopilot metrics for time-series forecasting.

  • Default objective metrics: AverageWeightedQuantileLoss

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

This field is required.

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.

The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

• For tabular problem types:

  • List of available metrics:

    • Regression: MAE, MSE, R2, RMSE

    • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

    For a description of each metric, see Autopilot metrics for classification and regression.

  • Default objective metrics:

    • Regression: MSE.

    • Binary classification: F1.

    • Multiclass classification: Accuracy.

• For image or text classification problem types:

  • List of available metrics: Accuracy

    For a description of each metric, see Autopilot metrics for text and image classification.

  • Default objective metrics: Accuracy

• For time-series forecasting problem types:

  • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE

    For a description of each metric, see Autopilot metrics for time-series forecasting.

  • Default objective metrics: AverageWeightedQuantileLoss

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

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.

The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

• For tabular problem types:

  • List of available metrics:

    • Regression: MAE, MSE, R2, RMSE

    • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

    For a description of each metric, see Autopilot metrics for classification and regression.

  • Default objective metrics:

    • Regression: MSE.

    • Binary classification: F1.

    • Multiclass classification: Accuracy.

• For image or text classification problem types:

  • List of available metrics: Accuracy

    For a description of each metric, see Autopilot metrics for text and image classification.

  • Default objective metrics: Accuracy

• For time-series forecasting problem types:

  • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE

    For a description of each metric, see Autopilot metrics for time-series forecasting.

  • Default objective metrics: AverageWeightedQuantileLoss

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

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 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