public static interface AutoMLJobObjective.Builder extends SdkPojo, CopyableBuilder<AutoMLJobObjective.Builder,AutoMLJobObjective>
| 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.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildAutoMLJobObjective.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:
For tabular problem types:
Regression: MSE.
Binary classification: F1.
Multiclass classification: Accuracy.
For image or text classification problem types: Accuracy
For time-series forecasting problem types: AverageWeightedQuantileLoss
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:
For tabular problem types:
Regression: MSE.
Binary classification: F1.
Multiclass classification: Accuracy.
For image or text classification problem types: Accuracy
For time-series forecasting problem types: AverageWeightedQuantileLoss
AutoMLMetricEnum,
AutoMLMetricEnumAutoMLJobObjective.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:
For tabular problem types:
Regression: MSE.
Binary classification: F1.
Multiclass classification: Accuracy.
For image or text classification problem types: Accuracy
For time-series forecasting problem types: AverageWeightedQuantileLoss
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:
For tabular problem types:
Regression: MSE.
Binary classification: F1.
Multiclass classification: Accuracy.
For image or text classification problem types: Accuracy
For time-series forecasting problem types: AverageWeightedQuantileLoss
AutoMLMetricEnum,
AutoMLMetricEnumCopyright © 2023. All rights reserved.