AWS SDK for C++  1.9.44
AWS SDK for C++
Public Member Functions | List of all members
Aws::FraudDetector::Model::TrainingMetrics Class Reference

#include <TrainingMetrics.h>

Public Member Functions

 TrainingMetrics ()
 
 TrainingMetrics (Aws::Utils::Json::JsonView jsonValue)
 
TrainingMetricsoperator= (Aws::Utils::Json::JsonView jsonValue)
 
Aws::Utils::Json::JsonValue Jsonize () const
 
double GetAuc () const
 
bool AucHasBeenSet () const
 
void SetAuc (double value)
 
TrainingMetricsWithAuc (double value)
 
const Aws::Vector< MetricDataPoint > & GetMetricDataPoints () const
 
bool MetricDataPointsHasBeenSet () const
 
void SetMetricDataPoints (const Aws::Vector< MetricDataPoint > &value)
 
void SetMetricDataPoints (Aws::Vector< MetricDataPoint > &&value)
 
TrainingMetricsWithMetricDataPoints (const Aws::Vector< MetricDataPoint > &value)
 
TrainingMetricsWithMetricDataPoints (Aws::Vector< MetricDataPoint > &&value)
 
TrainingMetricsAddMetricDataPoints (const MetricDataPoint &value)
 
TrainingMetricsAddMetricDataPoints (MetricDataPoint &&value)
 

Detailed Description

The training metric details.

See Also:

AWS API Reference

Definition at line 32 of file TrainingMetrics.h.

Constructor & Destructor Documentation

◆ TrainingMetrics() [1/2]

Aws::FraudDetector::Model::TrainingMetrics::TrainingMetrics ( )

◆ TrainingMetrics() [2/2]

Aws::FraudDetector::Model::TrainingMetrics::TrainingMetrics ( Aws::Utils::Json::JsonView  jsonValue)

Member Function Documentation

◆ AddMetricDataPoints() [1/2]

TrainingMetrics& Aws::FraudDetector::Model::TrainingMetrics::AddMetricDataPoints ( const MetricDataPoint value)
inline

The data points details.

Definition at line 107 of file TrainingMetrics.h.

◆ AddMetricDataPoints() [2/2]

TrainingMetrics& Aws::FraudDetector::Model::TrainingMetrics::AddMetricDataPoints ( MetricDataPoint &&  value)
inline

The data points details.

Definition at line 112 of file TrainingMetrics.h.

◆ AucHasBeenSet()

bool Aws::FraudDetector::Model::TrainingMetrics::AucHasBeenSet ( ) const
inline

The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.

Definition at line 55 of file TrainingMetrics.h.

◆ GetAuc()

double Aws::FraudDetector::Model::TrainingMetrics::GetAuc ( ) const
inline

The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.

Definition at line 47 of file TrainingMetrics.h.

◆ GetMetricDataPoints()

const Aws::Vector<MetricDataPoint>& Aws::FraudDetector::Model::TrainingMetrics::GetMetricDataPoints ( ) const
inline

The data points details.

Definition at line 77 of file TrainingMetrics.h.

◆ Jsonize()

Aws::Utils::Json::JsonValue Aws::FraudDetector::Model::TrainingMetrics::Jsonize ( ) const

◆ MetricDataPointsHasBeenSet()

bool Aws::FraudDetector::Model::TrainingMetrics::MetricDataPointsHasBeenSet ( ) const
inline

The data points details.

Definition at line 82 of file TrainingMetrics.h.

◆ operator=()

TrainingMetrics& Aws::FraudDetector::Model::TrainingMetrics::operator= ( Aws::Utils::Json::JsonView  jsonValue)

◆ SetAuc()

void Aws::FraudDetector::Model::TrainingMetrics::SetAuc ( double  value)
inline

The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.

Definition at line 63 of file TrainingMetrics.h.

◆ SetMetricDataPoints() [1/2]

void Aws::FraudDetector::Model::TrainingMetrics::SetMetricDataPoints ( Aws::Vector< MetricDataPoint > &&  value)
inline

The data points details.

Definition at line 92 of file TrainingMetrics.h.

◆ SetMetricDataPoints() [2/2]

void Aws::FraudDetector::Model::TrainingMetrics::SetMetricDataPoints ( const Aws::Vector< MetricDataPoint > &  value)
inline

The data points details.

Definition at line 87 of file TrainingMetrics.h.

◆ WithAuc()

TrainingMetrics& Aws::FraudDetector::Model::TrainingMetrics::WithAuc ( double  value)
inline

The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.

Definition at line 71 of file TrainingMetrics.h.

◆ WithMetricDataPoints() [1/2]

TrainingMetrics& Aws::FraudDetector::Model::TrainingMetrics::WithMetricDataPoints ( Aws::Vector< MetricDataPoint > &&  value)
inline

The data points details.

Definition at line 102 of file TrainingMetrics.h.

◆ WithMetricDataPoints() [2/2]

TrainingMetrics& Aws::FraudDetector::Model::TrainingMetrics::WithMetricDataPoints ( const Aws::Vector< MetricDataPoint > &  value)
inline

The data points details.

Definition at line 97 of file TrainingMetrics.h.


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