Interface AutoMLJobObjective.Builder
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- 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>
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description AutoMLJobObjective.Builder
metricName(String metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system.AutoMLJobObjective.Builder
metricName(AutoMLMetricEnum 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.utils.builder.CopyableBuilder
copy
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Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
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Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
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Method Detail
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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.
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
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Binary classification:
Accuracy
,AUC
,BalancedAccuracy
,F1
,Precision
,Recall
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Multiclass classification:
Accuracy
,BalancedAccuracy
,F1macro
,PrecisionMacro
,RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression.
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Default objective metrics:
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Regression:
MSE
. -
Binary classification:
F1
. -
Multiclass classification:
Accuracy
.
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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.
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Default objective metrics:
Accuracy
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For time-series forecasting problem types:
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List of available metrics:
RMSE
,wQL
,Average wQL
,MASE
,MAPE
,WAPE
For a description of each metric, see Autopilot metrics for time-series forecasting.
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Default objective metrics:
AverageWeightedQuantileLoss
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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.
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- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
AutoMLMetricEnum
,AutoMLMetricEnum
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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.
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
,AutoMLMetricEnum
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