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.LookoutVision.Types.ModelPerformance

Description

Synopsis

Documentation

data ModelPerformance Source #

Information about the evaluation performance of a trained model.

See: newModelPerformance smart constructor.

Constructors

ModelPerformance'
Fields recall :: Maybe Double The overall recall metric value for the trained model. precision :: Maybe Double The overall precision metric value for the trained model. f1Score :: Maybe Double The overall F1 score metric for the trained model.

Instances

Instances details

Eq ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Methods (==) :: ModelPerformance -> ModelPerformance -> Bool # (/=) :: ModelPerformance -> ModelPerformance -> Bool #
Read ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Methods readsPrec :: Int -> ReadS ModelPerformance # readList :: ReadS [ModelPerformance] # readPrec :: ReadPrec ModelPerformance # readListPrec :: ReadPrec [ModelPerformance] #
Show ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Methods showsPrec :: Int -> ModelPerformance -> ShowS # show :: ModelPerformance -> String # showList :: [ModelPerformance] -> ShowS #
Generic ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Associated Types type Rep ModelPerformance :: Type -> Type # Methods from :: ModelPerformance -> Rep ModelPerformance x # to :: Rep ModelPerformance x -> ModelPerformance #
NFData ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Methods rnf :: ModelPerformance -> () #
Hashable ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Methods hashWithSalt :: Int -> ModelPerformance -> Int # hash :: ModelPerformance -> Int #
FromJSON ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance Methods parseJSON :: Value -> Parser ModelPerformance # parseJSONList :: Value -> Parser [ModelPerformance] #
type Rep ModelPerformance Source #
Instance details Defined in Amazonka.LookoutVision.Types.ModelPerformance type Rep ModelPerformance = D1 ('MetaData "ModelPerformance" "Amazonka.LookoutVision.Types.ModelPerformance" "libZSservicesZSamazonka-lookoutvisionZSamazonka-lookoutvision" 'False) (C1 ('MetaCons "ModelPerformance'" 'PrefixI 'True) (S1 ('MetaSel ('Just "recall") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :: (S1 ('MetaSel ('Just "precision") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :: S1 ('MetaSel ('Just "f1Score") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)))))

newModelPerformance :: ModelPerformance Source #

Create a value of ModelPerformance 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:recall:ModelPerformance', modelPerformance_recall - The overall recall metric value for the trained model.

$sel:precision:ModelPerformance', modelPerformance_precision - The overall precision metric value for the trained model.

$sel:f1Score:ModelPerformance', modelPerformance_f1Score - The overall F1 score metric for the trained model.

modelPerformance_recall :: Lens' ModelPerformance (Maybe Double) Source #

The overall recall metric value for the trained model.

modelPerformance_precision :: Lens' ModelPerformance (Maybe Double) Source #

The overall precision metric value for the trained model.

modelPerformance_f1Score :: Lens' ModelPerformance (Maybe Double) Source #

The overall F1 score metric for the trained model.