public static interface GetEventPredictionResponse.Builder extends FraudDetectorResponse.Builder, SdkPojo, CopyableBuilder<GetEventPredictionResponse.Builder,GetEventPredictionResponse>
| Modifier and Type | Method and Description |
|---|---|
GetEventPredictionResponse.Builder |
modelScores(Collection<ModelScores> modelScores)
The model scores.
|
GetEventPredictionResponse.Builder |
modelScores(Consumer<ModelScores.Builder>... modelScores)
The model scores.
|
GetEventPredictionResponse.Builder |
modelScores(ModelScores... modelScores)
The model scores.
|
GetEventPredictionResponse.Builder |
ruleResults(Collection<RuleResult> ruleResults)
The results.
|
GetEventPredictionResponse.Builder |
ruleResults(Consumer<RuleResult.Builder>... ruleResults)
The results.
|
GetEventPredictionResponse.Builder |
ruleResults(RuleResult... ruleResults)
The results.
|
build, responseMetadata, responseMetadatasdkHttpResponse, sdkHttpResponseequalsBySdkFields, sdkFieldscopyapplyMutation, buildGetEventPredictionResponse.Builder modelScores(Collection<ModelScores> 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.
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.GetEventPredictionResponse.Builder modelScores(ModelScores... 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.
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.GetEventPredictionResponse.Builder modelScores(Consumer<ModelScores.Builder>... 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.
This is a convenience that creates an instance of theList.Builder avoiding the need to
create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #modelScores(List) .modelScores - a consumer that will call methods on List.Builder #modelScores(List) GetEventPredictionResponse.Builder ruleResults(Collection<RuleResult> ruleResults)
The results.
ruleResults - The results.GetEventPredictionResponse.Builder ruleResults(RuleResult... ruleResults)
The results.
ruleResults - The results.GetEventPredictionResponse.Builder ruleResults(Consumer<RuleResult.Builder>... ruleResults)
The results.
This is a convenience that creates an instance of theList.Builder avoiding the need to
create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #ruleResults(List) .ruleResults - a consumer that will call methods on List.Builder #ruleResults(List)