software.amazon.awssdk.services.sagemaker.model

Class AutoMLJobObjective

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.AutoMLJobObjective

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>

@Generated(value="software.amazon.awssdk:codegen")
public final class AutoMLJobObjective
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>

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

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	AutoMLJobObjective.Builder

Method Summary

Modifier and Type	Method and Description
static AutoMLJobObjective.Builder	builder()
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
AutoMLMetricEnum	metricName() The name of the objective metric used to measure the predictive quality of a machine learning system.
String	metricNameAsString() The name of the objective metric used to measure the predictive quality of a machine learning system.
List<SdkField<?>>	sdkFields()
static Class<? extends AutoMLJobObjective.Builder>	serializableBuilderClass()
AutoMLJobObjective.Builder	toBuilder()
String	toString() Returns a string representation of this object.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

metricName

public final 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: InferenceLatency, MAE, MSE, R2, RMSE

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

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, InferenceLatency, LogLoss, 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.

If the service returns an enum value that is not available in the current SDK version, metricName will return AutoMLMetricEnum.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from metricNameAsString().

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: InferenceLatency, MAE, MSE, R2, RMSE

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

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, InferenceLatency, LogLoss, 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

metricNameAsString

public final String metricNameAsString()

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: InferenceLatency, MAE, MSE, R2, RMSE

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

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, InferenceLatency, LogLoss, 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.

If the service returns an enum value that is not available in the current SDK version, metricName will return AutoMLMetricEnum.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from metricNameAsString().

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: InferenceLatency, MAE, MSE, R2, RMSE

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

    • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, InferenceLatency, LogLoss, 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

toBuilder

public AutoMLJobObjective.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>

builder

public static AutoMLJobObjective.Builder builder()

serializableBuilderClass

public static Class<? extends AutoMLJobObjective.Builder> serializableBuilderClass()

hashCode

public final int hashCode()

Overrides:

hashCode in class Object

equals

public final boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public final boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public final 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

getValueForField

public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)

sdkFields

public final List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo