Copyright	(c) 2013-2021 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay <brendan.g.hay+amazonka@gmail.com>
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	None

Amazonka.FraudDetector.Types.TrainingMetrics

Description

Synopsis

Documentation

data TrainingMetrics Source #

The training metric details.

See: newTrainingMetrics smart constructor.

Constructors

TrainingMetrics'
Fields auc :: Maybe Double 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. metricDataPoints :: Maybe [MetricDataPoint] The data points details.

Instances

Instances details

Eq TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Methods (==) :: TrainingMetrics -> TrainingMetrics -> Bool # (/=) :: TrainingMetrics -> TrainingMetrics -> Bool #
Read TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Methods readsPrec :: Int -> ReadS TrainingMetrics # readList :: ReadS [TrainingMetrics] # readPrec :: ReadPrec TrainingMetrics # readListPrec :: ReadPrec [TrainingMetrics] #
Show TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Methods showsPrec :: Int -> TrainingMetrics -> ShowS # show :: TrainingMetrics -> String # showList :: [TrainingMetrics] -> ShowS #
Generic TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Associated Types type Rep TrainingMetrics :: Type -> Type # Methods from :: TrainingMetrics -> Rep TrainingMetrics x # to :: Rep TrainingMetrics x -> TrainingMetrics #
NFData TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Methods rnf :: TrainingMetrics -> () #
Hashable TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Methods hashWithSalt :: Int -> TrainingMetrics -> Int # hash :: TrainingMetrics -> Int #
FromJSON TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics Methods parseJSON :: Value -> Parser TrainingMetrics # parseJSONList :: Value -> Parser [TrainingMetrics] #
type Rep TrainingMetrics Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingMetrics type Rep TrainingMetrics = D1 ('MetaData "TrainingMetrics" "Amazonka.FraudDetector.Types.TrainingMetrics" "libZSservicesZSamazonka-frauddetectorZSamazonka-frauddetector" 'False) (C1 ('MetaCons "TrainingMetrics'" 'PrefixI 'True) (S1 ('MetaSel ('Just "auc") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :*: S1 ('MetaSel ('Just "metricDataPoints") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [MetricDataPoint]))))

newTrainingMetrics :: TrainingMetrics Source #

Create a value of TrainingMetrics 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:auc:TrainingMetrics', trainingMetrics_auc - 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.

$sel:metricDataPoints:TrainingMetrics', trainingMetrics_metricDataPoints - The data points details.

trainingMetrics_auc :: Lens' TrainingMetrics (Maybe Double) Source #

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.

trainingMetrics_metricDataPoints :: Lens' TrainingMetrics (Maybe [MetricDataPoint]) Source #

The data points details.