Class AutoMLJobObjective
- All Implemented Interfaces:
Serializable
,SdkPojo
,ToCopyableBuilder<AutoMLJobObjective.Builder,
AutoMLJobObjective>
Specifies a metric to minimize or maximize as the objective of an AutoML job.
- See Also:
-
Nested Class Summary
-
Method Summary
Modifier and TypeMethodDescriptionstatic AutoMLJobObjective.Builder
builder()
final boolean
final boolean
equalsBySdkFields
(Object obj) Indicates whether some other object is "equal to" this one by SDK fields.final <T> Optional
<T> getValueForField
(String fieldName, Class<T> clazz) final int
hashCode()
final AutoMLMetricEnum
The name of the objective metric used to measure the predictive quality of a machine learning system.final String
The name of the objective metric used to measure the predictive quality of a machine learning system.static Class
<? extends AutoMLJobObjective.Builder> Take this object and create a builder that contains all of the current property values of this object.final String
toString()
Returns a string representation of this object.Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
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Method Details
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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.
If the service returns an enum value that is not available in the current SDK version,
metricName
will returnAutoMLMetricEnum.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available frommetricNameAsString()
.- 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:
-
-
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:
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.
If the service returns an enum value that is not available in the current SDK version,
metricName
will returnAutoMLMetricEnum.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available frommetricNameAsString()
.- 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:
-
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toBuilder
Description copied from interface:ToCopyableBuilder
Take this object and create a builder that contains all of the current property values of this object.- Specified by:
toBuilder
in interfaceToCopyableBuilder<AutoMLJobObjective.Builder,
AutoMLJobObjective> - Returns:
- a builder for type T
-
builder
-
serializableBuilderClass
-
hashCode
public final int hashCode() -
equals
-
equalsBySdkFields
Description copied from interface:SdkPojo
Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in anSdkPojo
class, and is generated based on a service model.If an
SdkPojo
class does not have any inherited fields,equalsBySdkFields
andequals
are essentially the same.- Specified by:
equalsBySdkFields
in interfaceSdkPojo
- Parameters:
obj
- the object to be compared with- Returns:
- true if the other object equals to this object by sdk fields, false otherwise.
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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. -
getValueForField
-
sdkFields
-