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Class: Aws::ForecastService::Types::DescribePredictorResponse

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_arnString

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

Returns:

  • (String)

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

#auto_ml_algorithm_arnsArray<String>

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

Returns:

  • (Array<String>)

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

#creation_timeTime

When the model training task was created.

Returns:

  • (Time)

    When the model training task was created.

#dataset_import_job_arnsArray<String>

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

Returns:

  • (Array<String>)

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

#encryption_configTypes::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.

Returns:

  • (Types::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.

#evaluation_parametersTypes::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.

Returns:

#featurization_configTypes::FeaturizationConfig

The featurization configuration.

Returns:

#forecast_horizonInteger

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

Returns:

  • (Integer)

    The number of time-steps of the forecast.

#hpo_configTypes::HyperParameterTuningJobConfig

The hyperparameter override values for the algorithm.

Returns:

#input_data_configTypes::InputDataConfig

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

Returns:

  • (Types::InputDataConfig)

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

#last_modification_timeTime

Initially, the same as CreationTime (status is CREATE_PENDING). Updated when training starts (status changed to CREATE_IN_PROGRESS), and when training is complete (status changed to ACTIVE) or fails (status changed to CREATE_FAILED).

Returns:

  • (Time)

    Initially, the same as CreationTime (status is CREATE_PENDING).

#messageString

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

Returns:

  • (String)

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

#perform_auto_mlBoolean

Whether the predictor is set to perform AutoML.

Returns:

  • (Boolean)

    Whether the predictor is set to perform AutoML.

#perform_hpoBoolean

Whether the predictor is set to perform HPO.

Returns:

  • (Boolean)

    Whether the predictor is set to perform HPO.

#predictor_arnString

The ARN of the predictor.

Returns:

  • (String)

    The ARN of the predictor.

#predictor_nameString

The name of the predictor.

Returns:

  • (String)

    The name of the predictor.

#statusString

The status of the predictor. States include:

  • ACTIVE

  • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

  • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

  • UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED

The Status of the predictor must be ACTIVE before using the predictor to create a forecast.

Returns:

  • (String)

    The status of the predictor.

#training_parametersHash<String,String>

The training parameters to override for model training. The parameters that you can override are listed in the individual algorithms in aws-forecast-choosing-recipes.

Returns:

  • (Hash<String,String>)

    The training parameters to override for model training.