Interface LabelSchema.Builder

All Superinterfaces:
Buildable, CopyableBuilder<LabelSchema.Builder,LabelSchema>, SdkBuilder<LabelSchema.Builder,LabelSchema>, SdkPojo
Enclosing class:
LabelSchema
@Mutable @NotThreadSafe public static interface LabelSchema.Builder extends SdkPojo, CopyableBuilder<LabelSchema.Builder,LabelSchema>

  • Method Details

    • labelMapper

      LabelSchema.Builder labelMapper(Map<String,? extends Collection<String>> labelMapper)

      The label mapper maps the Amazon Fraud Detector supported model classification labels (FRAUD, LEGIT) to the appropriate event type labels. For example, if "FRAUD" and " LEGIT" are Amazon Fraud Detector supported labels, this mapper could be: {"FRAUD" => ["0"], "LEGIT" => ["1"]} or {"FRAUD" => ["false"], "LEGIT" => ["true"]} or {"FRAUD" => ["fraud", "abuse"], "LEGIT" => ["legit", "safe"]}. The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.

      Parameters:
      labelMapper - The label mapper maps the Amazon Fraud Detector supported model classification labels ( FRAUD, LEGIT) to the appropriate event type labels. For example, if " FRAUD" and "LEGIT" are Amazon Fraud Detector supported labels, this mapper could be: {"FRAUD" => ["0"], "LEGIT" => ["1"]} or {"FRAUD" => ["false"], "LEGIT" => ["true"]} or {"FRAUD" => ["fraud", "abuse"], "LEGIT" => ["legit", "safe"]}. The value part of the mapper is a list, because you may have multiple label variants from your event type for a single Amazon Fraud Detector label.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • unlabeledEventsTreatment

      LabelSchema.Builder unlabeledEventsTreatment(String unlabeledEventsTreatment)

      The action to take for unlabeled events.

      • Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

      • Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

      • Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

      • Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

      By default, Amazon Fraud Detector ignores the unlabeled data.

      Parameters:
      unlabeledEventsTreatment - The action to take for unlabeled events.

      • Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

      • Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

      • Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

      • Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

      By default, Amazon Fraud Detector ignores the unlabeled data.

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

    • unlabeledEventsTreatment

      LabelSchema.Builder unlabeledEventsTreatment(UnlabeledEventsTreatment unlabeledEventsTreatment)

      The action to take for unlabeled events.

      • Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

      • Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

      • Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

      • Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

      By default, Amazon Fraud Detector ignores the unlabeled data.

      Parameters:
      unlabeledEventsTreatment - The action to take for unlabeled events.

      • Use IGNORE if you want the unlabeled events to be ignored. This is recommended when the majority of the events in the dataset are labeled.

      • Use FRAUD if you want to categorize all unlabeled events as “Fraud”. This is recommended when most of the events in your dataset are fraudulent.

      • Use LEGIT if you want to categorize all unlabeled events as “Legit”. This is recommended when most of the events in your dataset are legitimate.

      • Use AUTO if you want Amazon Fraud Detector to decide how to use the unlabeled data. This is recommended when there is significant unlabeled events in the dataset.

      By default, Amazon Fraud Detector ignores the unlabeled data.

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