See: newGetEventPrediction smart constructor.

Create a value of GetEventPrediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:detectorVersionId:GetEventPrediction', getEventPrediction_detectorVersionId - The detector version ID.

$sel:externalModelEndpointDataBlobs:GetEventPrediction', getEventPrediction_externalModelEndpointDataBlobs - The Amazon SageMaker model endpoint input data blobs.

$sel:detectorId:GetEventPrediction', getEventPrediction_detectorId - The detector ID.

$sel:eventId:GetEventPrediction', getEventPrediction_eventId - The unique ID used to identify the event.

$sel:eventTypeName:GetEventPrediction', getEventPrediction_eventTypeName - The event type associated with the detector specified for the prediction.

$sel:entities:GetEventPrediction', getEventPrediction_entities - The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."

$sel:eventTimestamp:GetEventPrediction', getEventPrediction_eventTimestamp - Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.

$sel:eventVariables:GetEventPrediction', getEventPrediction_eventVariables - Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.

You must provide at least one eventVariable

To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:

For Amazon Fraud Detector trained models:

If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.

For imported SageMaker models:

If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable.

The detector version ID.

The Amazon SageMaker model endpoint input data blobs.

The detector ID.

The unique ID used to identify the event.

The event type associated with the detector specified for the prediction.

The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."

Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.

Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.

You must provide at least one eventVariable

To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:

For Amazon Fraud Detector trained models:

If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.

For imported SageMaker models:

If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable.

See: newGetEventPredictionResponse smart constructor.

Create a value of GetEventPredictionResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:modelScores:GetEventPredictionResponse', getEventPredictionResponse_modelScores - The model scores. Amazon Fraud Detector generates model scores between 0 and 1000, where 0 is low fraud risk and 1000 is high fraud risk. Model scores are directly related to the false positive rate (FPR). For example, a score of 600 corresponds to an estimated 10% false positive rate whereas a score of 900 corresponds to an estimated 2% false positive rate.

$sel:externalModelOutputs:GetEventPredictionResponse', getEventPredictionResponse_externalModelOutputs - The model scores for Amazon SageMaker models.

$sel:ruleResults:GetEventPredictionResponse', getEventPredictionResponse_ruleResults - The results from the rules.

$sel:httpStatus:GetEventPredictionResponse', getEventPredictionResponse_httpStatus - The response's http status code.

The model scores. Amazon Fraud Detector generates model scores between 0 and 1000, where 0 is low fraud risk and 1000 is high fraud risk. Model scores are directly related to the false positive rate (FPR). For example, a score of 600 corresponds to an estimated 10% false positive rate whereas a score of 900 corresponds to an estimated 2% false positive rate.

The model scores for Amazon SageMaker models.

The results from the rules.

The response's http status code.