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.SageMaker.Types.AutoMLDataSplitConfig

Description

Synopsis

Documentation

data AutoMLDataSplitConfig Source #

This structure specifies how to split the data into train and validation datasets. The validation and training datasets must contain the same headers. The validation dataset must be less than 2 GB in size.

See: newAutoMLDataSplitConfig smart constructor.

Constructors

AutoMLDataSplitConfig'
Fields validationFraction :: Maybe Double The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

Instances

Instances details

FromJSON AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods parseJSON :: Value -> Parser AutoMLDataSplitConfig # parseJSONList :: Value -> Parser [AutoMLDataSplitConfig] #
ToJSON AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods toJSON :: AutoMLDataSplitConfig -> Value # toEncoding :: AutoMLDataSplitConfig -> Encoding # toJSONList :: [AutoMLDataSplitConfig] -> Value # toEncodingList :: [AutoMLDataSplitConfig] -> Encoding #
Generic AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Associated Types type Rep AutoMLDataSplitConfig :: Type -> Type # Methods from :: AutoMLDataSplitConfig -> Rep AutoMLDataSplitConfig x # to :: Rep AutoMLDataSplitConfig x -> AutoMLDataSplitConfig #
Read AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods readsPrec :: Int -> ReadS AutoMLDataSplitConfig # readList :: ReadS [AutoMLDataSplitConfig] # readPrec :: ReadPrec AutoMLDataSplitConfig # readListPrec :: ReadPrec [AutoMLDataSplitConfig] #
Show AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods showsPrec :: Int -> AutoMLDataSplitConfig -> ShowS # show :: AutoMLDataSplitConfig -> String # showList :: [AutoMLDataSplitConfig] -> ShowS #
NFData AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods rnf :: AutoMLDataSplitConfig -> () #
Eq AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods (==) :: AutoMLDataSplitConfig -> AutoMLDataSplitConfig -> Bool # (/=) :: AutoMLDataSplitConfig -> AutoMLDataSplitConfig -> Bool #
Hashable AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig Methods hashWithSalt :: Int -> AutoMLDataSplitConfig -> Int # hash :: AutoMLDataSplitConfig -> Int #
type Rep AutoMLDataSplitConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.AutoMLDataSplitConfig type Rep AutoMLDataSplitConfig = D1 ('MetaData "AutoMLDataSplitConfig" "Amazonka.SageMaker.Types.AutoMLDataSplitConfig" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" 'False) (C1 ('MetaCons "AutoMLDataSplitConfig'" 'PrefixI 'True) (S1 ('MetaSel ('Just "validationFraction") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double))))

newAutoMLDataSplitConfig :: AutoMLDataSplitConfig Source #

Create a value of AutoMLDataSplitConfig 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:validationFraction:AutoMLDataSplitConfig', autoMLDataSplitConfig_validationFraction - The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

autoMLDataSplitConfig_validationFraction :: Lens' AutoMLDataSplitConfig (Maybe Double) Source #

The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.