software.amazon.awssdk.services.sagemaker.model

Interface AutoMLJobObjective.Builder

All Superinterfaces:

Buildable, CopyableBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>, SdkBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>, SdkPojo

Enclosing class:

AutoMLJobObjective

public static interface AutoMLJobObjective.Builder
extends SdkPojo, CopyableBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>

Method Summary

Modifier and Type	Method and Description
AutoMLJobObjective.Builder	metricName(AutoMLMetricEnum metricName) The name of the objective metric used to measure the predictive quality of a machine learning system.
AutoMLJobObjective.Builder	metricName(String metricName) The name of the objective metric used to measure the predictive quality of a machine learning system.

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

equalsBySdkFields, sdkFields

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

copy

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

applyMutation, build

Method Detail

metricName

AutoMLJobObjective.Builder metricName(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.

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:

• MSE: for regression.

• F1: for binary classification

• Accuracy: for multiclass classification.

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.

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:

• MSE: for regression.

• F1: for binary classification

• Accuracy: for multiclass classification.

Returns:

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

See Also:

AutoMLMetricEnum, AutoMLMetricEnum

metricName

AutoMLJobObjective.Builder metricName(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.

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:

• MSE: for regression.

• F1: for binary classification

• Accuracy: for multiclass classification.

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.

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:

• MSE: for regression.

• F1: for binary classification

• Accuracy: for multiclass classification.

Returns:

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

See Also:

AutoMLMetricEnum, AutoMLMetricEnum