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.MachineLearning.Types.PerformanceMetrics

Description

Synopsis

data PerformanceMetrics = PerformanceMetrics' {
- properties :: Maybe (HashMap Text Text)
}
newPerformanceMetrics :: PerformanceMetrics
performanceMetrics_properties :: Lens' PerformanceMetrics (Maybe (HashMap Text Text))

Documentation

data PerformanceMetrics Source #

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

See: newPerformanceMetrics smart constructor.

Constructors

PerformanceMetrics'
Fields properties :: Maybe (HashMap Text Text)

Instances

Instances details

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

newPerformanceMetrics :: PerformanceMetrics Source #

Create a value of PerformanceMetrics 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:properties:PerformanceMetrics', performanceMetrics_properties - Undocumented member.

performanceMetrics_properties :: Lens' PerformanceMetrics (Maybe (HashMap Text Text)) Source #

Undocumented member.