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.Rekognition.Types.EvaluationResult

Description

Synopsis

Documentation

data EvaluationResult Source #

The evaluation results for the training of a model.

See: newEvaluationResult smart constructor.

Constructors

EvaluationResult'

Fields

summary :: Maybe Summary
The S3 bucket that contains the training summary.
f1Score :: Maybe Double
The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.

Instances

Instances details

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

newEvaluationResult :: EvaluationResult Source #

Create a value of EvaluationResult 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:summary:EvaluationResult', evaluationResult_summary - The S3 bucket that contains the training summary.

$sel:f1Score:EvaluationResult', evaluationResult_f1Score - The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.

evaluationResult_summary :: Lens' EvaluationResult (Maybe Summary) Source #

The S3 bucket that contains the training summary.

evaluationResult_f1Score :: Lens' EvaluationResult (Maybe Double) Source #

The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.