com.amazonaws.services.sagemaker.model

Class AutoMLJobObjective

java.lang.Object

com.amazonaws.services.sagemaker.model.AutoMLJobObjective

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLJobObjective
extends Object
implements Serializable, Cloneable, StructuredPojo

Specifies a metric to minimize or maximize as the objective of an AutoML job.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
AutoMLJobObjective()

Method Summary

Modifier and Type	Method and Description
AutoMLJobObjective	clone()
boolean	equals(Object obj)
String	getMetricName() The name of the objective metric used to measure the predictive quality of a machine learning system.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setMetricName(String metricName) The name of the objective metric used to measure the predictive quality of a machine learning system.
String	toString() Returns a string representation of this object.
AutoMLJobObjective	withMetricName(AutoMLMetricEnum metricName) The name of the objective metric used to measure the predictive quality of a machine learning system.
AutoMLJobObjective	withMetricName(String metricName) The name of the objective metric used to measure the predictive quality of a machine learning system.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AutoMLJobObjective

public AutoMLJobObjective()

Method Detail

setMetricName

public void setMetricName(String metricName)

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.

Parameters:

metricName - 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.

See Also:

AutoMLMetricEnum

getMetricName

public String getMetricName()

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.

Returns:

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.

See Also:

AutoMLMetricEnum

withMetricName

public AutoMLJobObjective withMetricName(String metricName)

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.

Parameters:

metricName - 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

AutoMLMetricEnum

withMetricName

public AutoMLJobObjective withMetricName(AutoMLMetricEnum metricName)

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.

Parameters:

metricName - 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

AutoMLMetricEnum

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public AutoMLJobObjective clone()

Overrides:

clone in class Object

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.