AWS SDK for C++  1.9.47
AWS SDK for C++
Public Member Functions | List of all members
Aws::FraudDetector::Model::GetEventPredictionResult Class Reference

#include <GetEventPredictionResult.h>

Public Member Functions

 GetEventPredictionResult ()
 
 GetEventPredictionResult (const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &result)
 
GetEventPredictionResultoperator= (const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &result)
 
const Aws::Vector< ModelScores > & GetModelScores () const
 
void SetModelScores (const Aws::Vector< ModelScores > &value)
 
void SetModelScores (Aws::Vector< ModelScores > &&value)
 
GetEventPredictionResultWithModelScores (const Aws::Vector< ModelScores > &value)
 
GetEventPredictionResultWithModelScores (Aws::Vector< ModelScores > &&value)
 
GetEventPredictionResultAddModelScores (const ModelScores &value)
 
GetEventPredictionResultAddModelScores (ModelScores &&value)
 
const Aws::Vector< RuleResult > & GetRuleResults () const
 
void SetRuleResults (const Aws::Vector< RuleResult > &value)
 
void SetRuleResults (Aws::Vector< RuleResult > &&value)
 
GetEventPredictionResultWithRuleResults (const Aws::Vector< RuleResult > &value)
 
GetEventPredictionResultWithRuleResults (Aws::Vector< RuleResult > &&value)
 
GetEventPredictionResultAddRuleResults (const RuleResult &value)
 
GetEventPredictionResultAddRuleResults (RuleResult &&value)
 

Detailed Description

Definition at line 29 of file GetEventPredictionResult.h.

Constructor & Destructor Documentation

◆ GetEventPredictionResult() [1/2]

Aws::FraudDetector::Model::GetEventPredictionResult::GetEventPredictionResult ( )

◆ GetEventPredictionResult() [2/2]

Aws::FraudDetector::Model::GetEventPredictionResult::GetEventPredictionResult ( const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &  result)

Member Function Documentation

◆ AddModelScores() [1/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::AddModelScores ( const ModelScores value)
inline

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.

Definition at line 89 of file GetEventPredictionResult.h.

◆ AddModelScores() [2/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::AddModelScores ( ModelScores &&  value)
inline

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.

Definition at line 98 of file GetEventPredictionResult.h.

◆ AddRuleResults() [1/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::AddRuleResults ( const RuleResult value)
inline

The results.

Definition at line 129 of file GetEventPredictionResult.h.

◆ AddRuleResults() [2/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::AddRuleResults ( RuleResult &&  value)
inline

The results.

Definition at line 134 of file GetEventPredictionResult.h.

◆ GetModelScores()

const Aws::Vector<ModelScores>& Aws::FraudDetector::Model::GetEventPredictionResult::GetModelScores ( ) const
inline

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.

Definition at line 44 of file GetEventPredictionResult.h.

◆ GetRuleResults()

const Aws::Vector<RuleResult>& Aws::FraudDetector::Model::GetEventPredictionResult::GetRuleResults ( ) const
inline

The results.

Definition at line 104 of file GetEventPredictionResult.h.

◆ operator=()

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::operator= ( const Aws::AmazonWebServiceResult< Aws::Utils::Json::JsonValue > &  result)

◆ SetModelScores() [1/2]

void Aws::FraudDetector::Model::GetEventPredictionResult::SetModelScores ( Aws::Vector< ModelScores > &&  value)
inline

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.

Definition at line 62 of file GetEventPredictionResult.h.

◆ SetModelScores() [2/2]

void Aws::FraudDetector::Model::GetEventPredictionResult::SetModelScores ( const Aws::Vector< ModelScores > &  value)
inline

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.

Definition at line 53 of file GetEventPredictionResult.h.

◆ SetRuleResults() [1/2]

void Aws::FraudDetector::Model::GetEventPredictionResult::SetRuleResults ( Aws::Vector< RuleResult > &&  value)
inline

The results.

Definition at line 114 of file GetEventPredictionResult.h.

◆ SetRuleResults() [2/2]

void Aws::FraudDetector::Model::GetEventPredictionResult::SetRuleResults ( const Aws::Vector< RuleResult > &  value)
inline

The results.

Definition at line 109 of file GetEventPredictionResult.h.

◆ WithModelScores() [1/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::WithModelScores ( Aws::Vector< ModelScores > &&  value)
inline

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.

Definition at line 80 of file GetEventPredictionResult.h.

◆ WithModelScores() [2/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::WithModelScores ( const Aws::Vector< ModelScores > &  value)
inline

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.

Definition at line 71 of file GetEventPredictionResult.h.

◆ WithRuleResults() [1/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::WithRuleResults ( Aws::Vector< RuleResult > &&  value)
inline

The results.

Definition at line 124 of file GetEventPredictionResult.h.

◆ WithRuleResults() [2/2]

GetEventPredictionResult& Aws::FraudDetector::Model::GetEventPredictionResult::WithRuleResults ( const Aws::Vector< RuleResult > &  value)
inline

The results.

Definition at line 119 of file GetEventPredictionResult.h.


The documentation for this class was generated from the following file: