software.amazon.awssdk.services.frauddetector.model

Interface LabelSchema.Builder

All Superinterfaces:

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

Enclosing class:

LabelSchema

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

Method Summary

Modifier and Type	Method and Description
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.
LabelSchema.Builder	unlabeledEventsTreatment(String unlabeledEventsTreatment) The action to take for unlabeled events.
LabelSchema.Builder	unlabeledEventsTreatment(UnlabeledEventsTreatment unlabeledEventsTreatment) The action to take for unlabeled events.

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

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, UnlabeledEventsTreatment

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:

UnlabeledEventsTreatment, UnlabeledEventsTreatment