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.TrainingResultV2

Description

Documentation

data TrainingResultV2 Source #

The training result details.

See: newTrainingResultV2 smart constructor.

Constructors

TrainingResultV2'

Fields

aggregatedVariablesImportanceMetrics :: Maybe AggregatedVariablesImportanceMetrics
The variable importance metrics of the aggregated variables.
Account Takeover Insights (ATI) model uses event variables from the login data you provide to continuously calculate a set of variables (aggregated variables) based on historical events. For example, your ATI model might calculate the number of times an user has logged in using the same IP address. In this case, event variables used to derive the aggregated variables are IP address and user.
dataValidationMetrics :: Maybe DataValidationMetrics
trainingMetricsV2 :: Maybe TrainingMetricsV2
The training metric details.
variableImportanceMetrics :: Maybe VariableImportanceMetrics

Instances

Instances details

FromJSON TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Methods parseJSON :: Value -> Parser TrainingResultV2 # parseJSONList :: Value -> Parser [TrainingResultV2] #
Generic TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Associated Types type Rep TrainingResultV2 :: Type -> Type # Methods from :: TrainingResultV2 -> Rep TrainingResultV2 x # to :: Rep TrainingResultV2 x -> TrainingResultV2 #
Read TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Methods readsPrec :: Int -> ReadS TrainingResultV2 # readList :: ReadS [TrainingResultV2] # readPrec :: ReadPrec TrainingResultV2 # readListPrec :: ReadPrec [TrainingResultV2] #
Show TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Methods showsPrec :: Int -> TrainingResultV2 -> ShowS # show :: TrainingResultV2 -> String # showList :: [TrainingResultV2] -> ShowS #
NFData TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Methods rnf :: TrainingResultV2 -> () #
Eq TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Methods (==) :: TrainingResultV2 -> TrainingResultV2 -> Bool # (/=) :: TrainingResultV2 -> TrainingResultV2 -> Bool #
Hashable TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 Methods hashWithSalt :: Int -> TrainingResultV2 -> Int # hash :: TrainingResultV2 -> Int #
type Rep TrainingResultV2 Source #
Instance details Defined in Amazonka.FraudDetector.Types.TrainingResultV2 type Rep TrainingResultV2 = D1 ('MetaData "TrainingResultV2" "Amazonka.FraudDetector.Types.TrainingResultV2" "amazonka-frauddetector-2.0-CdXFXtLV8DgKo4Kta7Jw61" 'False) (C1 ('MetaCons "TrainingResultV2'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "aggregatedVariablesImportanceMetrics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe AggregatedVariablesImportanceMetrics)) :: S1 ('MetaSel ('Just "dataValidationMetrics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe DataValidationMetrics))) :: (S1 ('MetaSel ('Just "trainingMetricsV2") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TrainingMetricsV2)) :*: S1 ('MetaSel ('Just "variableImportanceMetrics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe VariableImportanceMetrics)))))

newTrainingResultV2 :: TrainingResultV2 Source #

Create a value of TrainingResultV2 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:aggregatedVariablesImportanceMetrics:TrainingResultV2', trainingResultV2_aggregatedVariablesImportanceMetrics - The variable importance metrics of the aggregated variables.

Account Takeover Insights (ATI) model uses event variables from the login data you provide to continuously calculate a set of variables (aggregated variables) based on historical events. For example, your ATI model might calculate the number of times an user has logged in using the same IP address. In this case, event variables used to derive the aggregated variables are IP address and user.

$sel:dataValidationMetrics:TrainingResultV2', trainingResultV2_dataValidationMetrics - Undocumented member.

$sel:trainingMetricsV2:TrainingResultV2', trainingResultV2_trainingMetricsV2 - The training metric details.

$sel:variableImportanceMetrics:TrainingResultV2', trainingResultV2_variableImportanceMetrics - Undocumented member.

trainingResultV2_aggregatedVariablesImportanceMetrics :: Lens' TrainingResultV2 (Maybe AggregatedVariablesImportanceMetrics) Source #

The variable importance metrics of the aggregated variables.

Account Takeover Insights (ATI) model uses event variables from the login data you provide to continuously calculate a set of variables (aggregated variables) based on historical events. For example, your ATI model might calculate the number of times an user has logged in using the same IP address. In this case, event variables used to derive the aggregated variables are IP address and user.

trainingResultV2_dataValidationMetrics :: Lens' TrainingResultV2 (Maybe DataValidationMetrics) Source #

Undocumented member.

trainingResultV2_trainingMetricsV2 :: Lens' TrainingResultV2 (Maybe TrainingMetricsV2) Source #

The training metric details.

trainingResultV2_variableImportanceMetrics :: Lens' TrainingResultV2 (Maybe VariableImportanceMetrics) Source #

Undocumented member.