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, responseMetadata
sdkHttpResponse, sdkHttpResponse
equalsBySdkFields, sdkFields
copy
applyMutation, build
GetEventPredictionResponse.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)
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