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.Forecast.DescribePredictor

Contents

Creating a Request
Request Lenses
Destructuring the Response
Response Lenses

Description

This operation is only valid for legacy predictors created with CreatePredictor. If you are not using a legacy predictor, use DescribeAutoPredictor.

Describes a predictor created using the CreatePredictor operation.

In addition to listing the properties provided in the CreatePredictor request, this operation lists the following properties:

DatasetImportJobArns - The dataset import jobs used to import training data.
AutoMLAlgorithmArns - If AutoML is performed, the algorithms that were evaluated.
```
CreationTime
```
```
LastModificationTime
```
```
Status
```
Message - If an error occurred, information about the error.

Synopsis

Creating a Request

data DescribePredictor Source #

See: newDescribePredictor smart constructor.

Constructors

DescribePredictor'
Fields predictorArn :: Text The Amazon Resource Name (ARN) of the predictor that you want information about.

Instances

Instances details

ToJSON DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods toJSON :: DescribePredictor -> Value # toEncoding :: DescribePredictor -> Encoding # toJSONList :: [DescribePredictor] -> Value # toEncodingList :: [DescribePredictor] -> Encoding #
ToHeaders DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods toHeaders :: DescribePredictor -> [Header] #
ToPath DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods toPath :: DescribePredictor -> ByteString #
ToQuery DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods toQuery :: DescribePredictor -> QueryString #
AWSRequest DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Associated Types type AWSResponse DescribePredictor # Methods request :: (Service -> Service) -> DescribePredictor -> Request DescribePredictor # response :: MonadResource m => (ByteStringLazy -> IO ByteStringLazy) -> Service -> Proxy DescribePredictor -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribePredictor))) #
Generic DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Associated Types type Rep DescribePredictor :: Type -> Type # Methods from :: DescribePredictor -> Rep DescribePredictor x # to :: Rep DescribePredictor x -> DescribePredictor #
Read DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods readsPrec :: Int -> ReadS DescribePredictor # readList :: ReadS [DescribePredictor] # readPrec :: ReadPrec DescribePredictor # readListPrec :: ReadPrec [DescribePredictor] #
Show DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods showsPrec :: Int -> DescribePredictor -> ShowS # show :: DescribePredictor -> String # showList :: [DescribePredictor] -> ShowS #
NFData DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods rnf :: DescribePredictor -> () #
Eq DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods (==) :: DescribePredictor -> DescribePredictor -> Bool # (/=) :: DescribePredictor -> DescribePredictor -> Bool #
Hashable DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods hashWithSalt :: Int -> DescribePredictor -> Int # hash :: DescribePredictor -> Int #
type AWSResponse DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor type AWSResponse DescribePredictor = DescribePredictorResponse
type Rep DescribePredictor Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor type Rep DescribePredictor = D1 ('MetaData "DescribePredictor" "Amazonka.Forecast.DescribePredictor" "amazonka-forecast-2.0-HHvJwvxGrDPBJtUcnmLBqf" 'False) (C1 ('MetaCons "DescribePredictor'" 'PrefixI 'True) (S1 ('MetaSel ('Just "predictorArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newDescribePredictor Source #

Arguments

:: Text	`DescribePredictor`
-> DescribePredictor

Create a value of DescribePredictor 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:

DescribePredictor, describePredictor_predictorArn - The Amazon Resource Name (ARN) of the predictor that you want information about.

Request Lenses

describePredictor_predictorArn :: Lens' DescribePredictor Text Source #

The Amazon Resource Name (ARN) of the predictor that you want information about.

Destructuring the Response

data DescribePredictorResponse Source #

See: newDescribePredictorResponse smart constructor.

Constructors

DescribePredictorResponse'

Fields

algorithmArn :: Maybe Text
The Amazon Resource Name (ARN) of the algorithm used for model training.
autoMLAlgorithmArns :: Maybe [Text]
When PerformAutoML is specified, the ARN of the chosen algorithm.
autoMLOverrideStrategy :: Maybe AutoMLOverrideStrategy
The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.
The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.
This parameter is only valid for predictors trained using AutoML.
creationTime :: Maybe POSIX
When the model training task was created.
datasetImportJobArns :: Maybe [Text]
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
encryptionConfig :: Maybe EncryptionConfig
An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
estimatedTimeRemainingInMinutes :: Maybe Integer
The estimated time remaining in minutes for the predictor training job to complete.
evaluationParameters :: Maybe EvaluationParameters
Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.
featurizationConfig :: Maybe FeaturizationConfig
The featurization configuration.
forecastHorizon :: Maybe Int
The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
forecastTypes :: Maybe (NonEmpty Text)
The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"]
hPOConfig :: Maybe HyperParameterTuningJobConfig
The hyperparameter override values for the algorithm.
inputDataConfig :: Maybe InputDataConfig
Describes the dataset group that contains the data to use to train the predictor.
isAutoPredictor :: Maybe Bool
Whether the predictor was created with CreateAutoPredictor.
lastModificationTime :: Maybe POSIX
The last time the resource was modified. The timestamp depends on the status of the job:
- CREATE_PENDING - The CreationTime.
- CREATE_IN_PROGRESS - The current timestamp.
- CREATE_STOPPING - The current timestamp.
- CREATE_STOPPED - When the job stopped.
- ACTIVE or CREATE_FAILED - When the job finished or failed.
message :: Maybe Text
If an error occurred, an informational message about the error.
optimizationMetric :: Maybe OptimizationMetric
The accuracy metric used to optimize the predictor.
performAutoML :: Maybe Bool
Whether the predictor is set to perform AutoML.
performHPO :: Maybe Bool
Whether the predictor is set to perform hyperparameter optimization (HPO).
predictorArn :: Maybe Text
The ARN of the predictor.
predictorExecutionDetails :: Maybe PredictorExecutionDetails
Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.
predictorName :: Maybe Text
The name of the predictor.
status :: Maybe Text
The status of the predictor. States include:
- ```
ACTIVE
```
- CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
- DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
- CREATE_STOPPING, CREATE_STOPPED
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
trainingParameters :: Maybe (HashMap Text Text)
The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
httpStatus :: Int
The response's http status code.

Instances

Instances details

Generic DescribePredictorResponse Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Associated Types type Rep DescribePredictorResponse :: Type -> Type # Methods from :: DescribePredictorResponse -> Rep DescribePredictorResponse x # to :: Rep DescribePredictorResponse x -> DescribePredictorResponse #
Read DescribePredictorResponse Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods readsPrec :: Int -> ReadS DescribePredictorResponse # readList :: ReadS [DescribePredictorResponse] # readPrec :: ReadPrec DescribePredictorResponse # readListPrec :: ReadPrec [DescribePredictorResponse] #
Show DescribePredictorResponse Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods showsPrec :: Int -> DescribePredictorResponse -> ShowS # show :: DescribePredictorResponse -> String # showList :: [DescribePredictorResponse] -> ShowS #
NFData DescribePredictorResponse Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods rnf :: DescribePredictorResponse -> () #
Eq DescribePredictorResponse Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor Methods (==) :: DescribePredictorResponse -> DescribePredictorResponse -> Bool # (/=) :: DescribePredictorResponse -> DescribePredictorResponse -> Bool #
type Rep DescribePredictorResponse Source #
Instance details Defined in Amazonka.Forecast.DescribePredictor type Rep DescribePredictorResponse = D1 ('MetaData "DescribePredictorResponse" "Amazonka.Forecast.DescribePredictor" "amazonka-forecast-2.0-HHvJwvxGrDPBJtUcnmLBqf" 'False) (C1 ('MetaCons "DescribePredictorResponse'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "algorithmArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "autoMLAlgorithmArns") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [Text])) :: S1 ('MetaSel ('Just "autoMLOverrideStrategy") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe AutoMLOverrideStrategy)))) :: (S1 ('MetaSel ('Just "creationTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: (S1 ('MetaSel ('Just "datasetImportJobArns") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [Text])) :: S1 ('MetaSel ('Just "encryptionConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EncryptionConfig))))) :: ((S1 ('MetaSel ('Just "estimatedTimeRemainingInMinutes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: (S1 ('MetaSel ('Just "evaluationParameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EvaluationParameters)) :: S1 ('MetaSel ('Just "featurizationConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe FeaturizationConfig)))) :: (S1 ('MetaSel ('Just "forecastHorizon") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Int)) :: (S1 ('MetaSel ('Just "forecastTypes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (NonEmpty Text))) :: S1 ('MetaSel ('Just "hPOConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe HyperParameterTuningJobConfig)))))) :: (((S1 ('MetaSel ('Just "inputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe InputDataConfig)) :: (S1 ('MetaSel ('Just "isAutoPredictor") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :: S1 ('MetaSel ('Just "lastModificationTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "optimizationMetric") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe OptimizationMetric)) :: S1 ('MetaSel ('Just "performAutoML") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool))))) :: ((S1 ('MetaSel ('Just "performHPO") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :: (S1 ('MetaSel ('Just "predictorArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "predictorExecutionDetails") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe PredictorExecutionDetails)))) :: ((S1 ('MetaSel ('Just "predictorName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "trainingParameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))))))

newDescribePredictorResponse Source #

Arguments

:: Int	`$sel:httpStatus:DescribePredictorResponse'`
-> DescribePredictorResponse

Create a value of DescribePredictorResponse 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:

DescribePredictorResponse, describePredictorResponse_algorithmArn - The Amazon Resource Name (ARN) of the algorithm used for model training.

$sel:autoMLAlgorithmArns:DescribePredictorResponse', describePredictorResponse_autoMLAlgorithmArns - When PerformAutoML is specified, the ARN of the chosen algorithm.

$sel:autoMLOverrideStrategy:DescribePredictorResponse', describePredictorResponse_autoMLOverrideStrategy - The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

This parameter is only valid for predictors trained using AutoML.

DescribePredictorResponse, describePredictorResponse_creationTime - When the model training task was created.

$sel:datasetImportJobArns:DescribePredictorResponse', describePredictorResponse_datasetImportJobArns - An array of the ARNs of the dataset import jobs used to import training data for the predictor.

$sel:encryptionConfig:DescribePredictorResponse', describePredictorResponse_encryptionConfig - An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

$sel:estimatedTimeRemainingInMinutes:DescribePredictorResponse', describePredictorResponse_estimatedTimeRemainingInMinutes - The estimated time remaining in minutes for the predictor training job to complete.

$sel:evaluationParameters:DescribePredictorResponse', describePredictorResponse_evaluationParameters - Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

$sel:featurizationConfig:DescribePredictorResponse', describePredictorResponse_featurizationConfig - The featurization configuration.

$sel:forecastHorizon:DescribePredictorResponse', describePredictorResponse_forecastHorizon - The number of time-steps of the forecast. The forecast horizon is also called the prediction length.

$sel:forecastTypes:DescribePredictorResponse', describePredictorResponse_forecastTypes - The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"]

$sel:hPOConfig:DescribePredictorResponse', describePredictorResponse_hPOConfig - The hyperparameter override values for the algorithm.

$sel:inputDataConfig:DescribePredictorResponse', describePredictorResponse_inputDataConfig - Describes the dataset group that contains the data to use to train the predictor.

DescribePredictorResponse, describePredictorResponse_isAutoPredictor - Whether the predictor was created with CreateAutoPredictor.

DescribePredictorResponse, describePredictorResponse_lastModificationTime - The last time the resource was modified. The timestamp depends on the status of the job:

CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.

DescribePredictorResponse, describePredictorResponse_message - If an error occurred, an informational message about the error.

$sel:optimizationMetric:DescribePredictorResponse', describePredictorResponse_optimizationMetric - The accuracy metric used to optimize the predictor.

$sel:performAutoML:DescribePredictorResponse', describePredictorResponse_performAutoML - Whether the predictor is set to perform AutoML.

$sel:performHPO:DescribePredictorResponse', describePredictorResponse_performHPO - Whether the predictor is set to perform hyperparameter optimization (HPO).

DescribePredictor, describePredictorResponse_predictorArn - The ARN of the predictor.

$sel:predictorExecutionDetails:DescribePredictorResponse', describePredictorResponse_predictorExecutionDetails - Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.

DescribePredictorResponse, describePredictorResponse_predictorName - The name of the predictor.

DescribePredictorResponse, describePredictorResponse_status - The status of the predictor. States include:

```
ACTIVE
```
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
CREATE_STOPPING, CREATE_STOPPED

The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.

$sel:trainingParameters:DescribePredictorResponse', describePredictorResponse_trainingParameters - The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.

$sel:httpStatus:DescribePredictorResponse', describePredictorResponse_httpStatus - The response's http status code.

Response Lenses

describePredictorResponse_algorithmArn :: Lens' DescribePredictorResponse (Maybe Text) Source #

The Amazon Resource Name (ARN) of the algorithm used for model training.

describePredictorResponse_autoMLAlgorithmArns :: Lens' DescribePredictorResponse (Maybe [Text]) Source #

When PerformAutoML is specified, the ARN of the chosen algorithm.

describePredictorResponse_autoMLOverrideStrategy :: Lens' DescribePredictorResponse (Maybe AutoMLOverrideStrategy) Source #

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

This parameter is only valid for predictors trained using AutoML.

describePredictorResponse_creationTime :: Lens' DescribePredictorResponse (Maybe UTCTime) Source #

When the model training task was created.

describePredictorResponse_datasetImportJobArns :: Lens' DescribePredictorResponse (Maybe [Text]) Source #

An array of the ARNs of the dataset import jobs used to import training data for the predictor.

describePredictorResponse_encryptionConfig :: Lens' DescribePredictorResponse (Maybe EncryptionConfig) Source #

An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

describePredictorResponse_estimatedTimeRemainingInMinutes :: Lens' DescribePredictorResponse (Maybe Integer) Source #

The estimated time remaining in minutes for the predictor training job to complete.

describePredictorResponse_evaluationParameters :: Lens' DescribePredictorResponse (Maybe EvaluationParameters) Source #

Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

describePredictorResponse_featurizationConfig :: Lens' DescribePredictorResponse (Maybe FeaturizationConfig) Source #

The featurization configuration.

describePredictorResponse_forecastHorizon :: Lens' DescribePredictorResponse (Maybe Int) Source #

The number of time-steps of the forecast. The forecast horizon is also called the prediction length.

describePredictorResponse_forecastTypes :: Lens' DescribePredictorResponse (Maybe (NonEmpty Text)) Source #

The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"]

describePredictorResponse_hPOConfig :: Lens' DescribePredictorResponse (Maybe HyperParameterTuningJobConfig) Source #

The hyperparameter override values for the algorithm.

describePredictorResponse_inputDataConfig :: Lens' DescribePredictorResponse (Maybe InputDataConfig) Source #

Describes the dataset group that contains the data to use to train the predictor.

describePredictorResponse_isAutoPredictor :: Lens' DescribePredictorResponse (Maybe Bool) Source #

Whether the predictor was created with CreateAutoPredictor.

describePredictorResponse_lastModificationTime :: Lens' DescribePredictorResponse (Maybe UTCTime) Source #

The last time the resource was modified. The timestamp depends on the status of the job:

CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.

describePredictorResponse_message :: Lens' DescribePredictorResponse (Maybe Text) Source #

If an error occurred, an informational message about the error.

describePredictorResponse_optimizationMetric :: Lens' DescribePredictorResponse (Maybe OptimizationMetric) Source #

The accuracy metric used to optimize the predictor.

describePredictorResponse_performAutoML :: Lens' DescribePredictorResponse (Maybe Bool) Source #

Whether the predictor is set to perform AutoML.

describePredictorResponse_performHPO :: Lens' DescribePredictorResponse (Maybe Bool) Source #

Whether the predictor is set to perform hyperparameter optimization (HPO).

describePredictorResponse_predictorArn :: Lens' DescribePredictorResponse (Maybe Text) Source #

The ARN of the predictor.

describePredictorResponse_predictorExecutionDetails :: Lens' DescribePredictorResponse (Maybe PredictorExecutionDetails) Source #

Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.

describePredictorResponse_predictorName :: Lens' DescribePredictorResponse (Maybe Text) Source #

The name of the predictor.

describePredictorResponse_status :: Lens' DescribePredictorResponse (Maybe Text) Source #

The status of the predictor. States include:

```
ACTIVE
```
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
CREATE_STOPPING, CREATE_STOPPED

The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.

describePredictorResponse_trainingParameters :: Lens' DescribePredictorResponse (Maybe (HashMap Text Text)) Source #

The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.

describePredictorResponse_httpStatus :: Lens' DescribePredictorResponse Int Source #

The response's http status code.