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.PredictionExplanations

Description

Documentation

data PredictionExplanations Source #

The prediction explanations that provide insight into how each event variable impacted the model version's fraud prediction score.

See: newPredictionExplanations smart constructor.

Constructors

PredictionExplanations'

Fields

aggregatedVariablesImpactExplanations :: Maybe [AggregatedVariablesImpactExplanation]
The details of the aggregated variables impact on the prediction score.
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.
variableImpactExplanations :: Maybe [VariableImpactExplanation]
The details of the event variable's impact on the prediction score.

Instances

Instances details

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

newPredictionExplanations :: PredictionExplanations Source #

Create a value of PredictionExplanations 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:aggregatedVariablesImpactExplanations:PredictionExplanations', predictionExplanations_aggregatedVariablesImpactExplanations - The details of the aggregated variables impact on the prediction score.

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:variableImpactExplanations:PredictionExplanations', predictionExplanations_variableImpactExplanations - The details of the event variable's impact on the prediction score.

predictionExplanations_aggregatedVariablesImpactExplanations :: Lens' PredictionExplanations (Maybe [AggregatedVariablesImpactExplanation]) Source #

The details of the aggregated variables impact on the prediction score.

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.

predictionExplanations_variableImpactExplanations :: Lens' PredictionExplanations (Maybe [VariableImpactExplanation]) Source #

The details of the event variable's impact on the prediction score.