libZSservicesZSamazonka-frauddetectorZSamazonka-frauddetector
Copyright(c) 2013-2021 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone

Amazonka.FraudDetector.Types.MetricDataPoint

Description

 
Synopsis

Documentation

data MetricDataPoint Source #

Model performance metrics data points.

See: newMetricDataPoint smart constructor.

Constructors

MetricDataPoint' 

Fields

  • precision :: Maybe Double

    The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.

  • fpr :: Maybe Double

    The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.

  • threshold :: Maybe Double

    The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.

  • tpr :: Maybe Double

    The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.

Instances

Instances details
Eq MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Methods

(==) :: MetricDataPoint -> MetricDataPoint -> Bool #

(/=) :: MetricDataPoint -> MetricDataPoint -> Bool #

Read MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Methods

readsPrec :: Int -> ReadS MetricDataPoint #

readList :: ReadS [MetricDataPoint] #

readPrec :: ReadPrec MetricDataPoint #

readListPrec :: ReadPrec [MetricDataPoint] #

Show MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Methods

showsPrec :: Int -> MetricDataPoint -> ShowS #

show :: MetricDataPoint -> String #

showList :: [MetricDataPoint] -> ShowS #

Generic MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Associated Types

type Rep MetricDataPoint :: Type -> Type #

Methods

from :: MetricDataPoint -> Rep MetricDataPoint x #

to :: Rep MetricDataPoint x -> MetricDataPoint #

NFData MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Methods

rnf :: MetricDataPoint -> () #

Hashable MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Methods

hashWithSalt :: Int -> MetricDataPoint -> Int #

hash :: MetricDataPoint -> Int #

FromJSON MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

Methods

parseJSON :: Value -> Parser MetricDataPoint #

parseJSONList :: Value -> Parser [MetricDataPoint] #

type Rep MetricDataPoint Source # 
Instance details

Defined in Amazonka.FraudDetector.Types.MetricDataPoint

type Rep MetricDataPoint = D1 ('MetaData "MetricDataPoint" "Amazonka.FraudDetector.Types.MetricDataPoint" "libZSservicesZSamazonka-frauddetectorZSamazonka-frauddetector" 'False) (C1 ('MetaCons "MetricDataPoint'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "precision") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :*: S1 ('MetaSel ('Just "fpr") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double))) :*: (S1 ('MetaSel ('Just "threshold") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :*: S1 ('MetaSel ('Just "tpr") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)))))

newMetricDataPoint :: MetricDataPoint Source #

Create a value of MetricDataPoint 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:precision:MetricDataPoint', metricDataPoint_precision - The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.

$sel:fpr:MetricDataPoint', metricDataPoint_fpr - The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.

$sel:threshold:MetricDataPoint', metricDataPoint_threshold - The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.

$sel:tpr:MetricDataPoint', metricDataPoint_tpr - The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.

metricDataPoint_precision :: Lens' MetricDataPoint (Maybe Double) Source #

The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.

metricDataPoint_fpr :: Lens' MetricDataPoint (Maybe Double) Source #

The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.

metricDataPoint_threshold :: Lens' MetricDataPoint (Maybe Double) Source #

The model threshold that specifies an acceptable fraud capture rate. For example, a threshold of 500 means any model score 500 or above is labeled as fraud.

metricDataPoint_tpr :: Lens' MetricDataPoint (Maybe Double) Source #

The true positive rate. This is the percentage of total fraud the model detects. Also known as capture rate.