Copyright	(c) 2013-2023 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	Safe-Inferred
Language	Haskell2010

Amazonka.FraudDetector.Types.MetricDataPoint

Description

Synopsis

Documentation

data MetricDataPoint Source #

Model performance metrics data points.

See: newMetricDataPoint smart constructor.

Constructors

MetricDataPoint'

Fields

fpr :: Maybe Double
The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.
precision :: Maybe Double
The percentage of fraud events correctly predicted as fraudulent as compared to all events predicted as fraudulent.
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

FromJSON MetricDataPoint Source #
Instance details Defined in Amazonka.FraudDetector.Types.MetricDataPoint Methods parseJSON :: Value -> Parser MetricDataPoint # parseJSONList :: Value -> Parser [MetricDataPoint] #
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 #
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 #
NFData MetricDataPoint Source #
Instance details Defined in Amazonka.FraudDetector.Types.MetricDataPoint Methods rnf :: MetricDataPoint -> () #
Eq MetricDataPoint Source #
Instance details Defined in Amazonka.FraudDetector.Types.MetricDataPoint Methods (==) :: MetricDataPoint -> MetricDataPoint -> Bool # (/=) :: MetricDataPoint -> MetricDataPoint -> Bool #
Hashable MetricDataPoint Source #
Instance details Defined in Amazonka.FraudDetector.Types.MetricDataPoint Methods hashWithSalt :: Int -> MetricDataPoint -> Int # hash :: MetricDataPoint -> Int #
type Rep MetricDataPoint Source #
Instance details Defined in Amazonka.FraudDetector.Types.MetricDataPoint type Rep MetricDataPoint = D1 ('MetaData "MetricDataPoint" "Amazonka.FraudDetector.Types.MetricDataPoint" "amazonka-frauddetector-2.0-CdXFXtLV8DgKo4Kta7Jw61" 'False) (C1 ('MetaCons "MetricDataPoint'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "fpr") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :: S1 ('MetaSel ('Just "precision") '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:fpr:MetricDataPoint', metricDataPoint_fpr - The false positive rate. This is the percentage of total legitimate events that are incorrectly predicted as fraud.

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

$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_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_precision :: Lens' MetricDataPoint (Maybe Double) Source #

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

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.