You are viewing documentation for version 2 of the AWS SDK for Ruby. Version 3 documentation can be found here.
Class: Aws::ForecastService::Types::DescribePredictorResponse
- Inherits:
-
Struct
- Object
- Struct
- Aws::ForecastService::Types::DescribePredictorResponse
- Defined in:
- (unknown)
Overview
Returned by:
Instance Attribute Summary collapse
-
#algorithm_arn ⇒ String
The Amazon Resource Name (ARN) of the algorithm used for model training.
-
#auto_ml_algorithm_arns ⇒ Array<String>
When
PerformAutoML
is specified, the ARN of the chosen algorithm. -
#creation_time ⇒ Time
When the model training task was created.
-
#dataset_import_job_arns ⇒ Array<String>
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
-
#encryption_config ⇒ 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_parameters ⇒ Types::EvaluationParameters
Used to override the default evaluation parameters of the specified algorithm.
-
#featurization_config ⇒ Types::FeaturizationConfig
The featurization configuration.
-
#forecast_horizon ⇒ Integer
The number of time-steps of the forecast.
-
#forecast_types ⇒ Array<String>
The forecast types used during predictor training.
-
#hpo_config ⇒ Types::HyperParameterTuningJobConfig
The hyperparameter override values for the algorithm.
-
#input_data_config ⇒ Types::InputDataConfig
Describes the dataset group that contains the data to use to train the predictor.
-
#last_modification_time ⇒ Time
Initially, the same as
CreationTime
(when the status isCREATE_PENDING
). -
#message ⇒ String
If an error occurred, an informational message about the error.
-
#perform_auto_ml ⇒ Boolean
Whether the predictor is set to perform AutoML.
-
#perform_hpo ⇒ Boolean
Whether the predictor is set to perform hyperparameter optimization (HPO).
-
#predictor_arn ⇒ String
The ARN of the predictor.
-
#predictor_execution_details ⇒ Types::PredictorExecutionDetails
Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor.
-
#predictor_name ⇒ String
The name of the predictor.
-
#status ⇒ String
The status of the predictor.
-
#training_parameters ⇒ Hash<String,String>
The default training parameters or overrides selected during model training.
Instance Attribute Details
#algorithm_arn ⇒ String
The Amazon Resource Name (ARN) of the algorithm used for model training.
#auto_ml_algorithm_arns ⇒ Array<String>
When PerformAutoML
is specified, the ARN of the chosen algorithm.
#creation_time ⇒ Time
When the model training task was created.
#dataset_import_job_arns ⇒ Array<String>
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
#encryption_config ⇒ 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_parameters ⇒ Types::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.
#featurization_config ⇒ Types::FeaturizationConfig
The featurization configuration.
#forecast_horizon ⇒ Integer
The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
#forecast_types ⇒ Array<String>
The forecast types used during predictor training. Default value is
["0.1","0.5","0.9"]
#hpo_config ⇒ Types::HyperParameterTuningJobConfig
The hyperparameter override values for the algorithm.
#input_data_config ⇒ Types::InputDataConfig
Describes the dataset group that contains the data to use to train the predictor.
#last_modification_time ⇒ Time
Initially, the same as CreationTime
(when the status is
CREATE_PENDING
). This value is updated when training starts (when the
status changes to CREATE_IN_PROGRESS
), and when training has completed
(when the status changes to ACTIVE
) or fails (when the status changes
to CREATE_FAILED
).
#message ⇒ String
If an error occurred, an informational message about the error.
#perform_auto_ml ⇒ Boolean
Whether the predictor is set to perform AutoML.
#perform_hpo ⇒ Boolean
Whether the predictor is set to perform hyperparameter optimization (HPO).
#predictor_arn ⇒ String
The ARN of the predictor.
#predictor_execution_details ⇒ Types::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.
#predictor_name ⇒ String
The name of the predictor.
#status ⇒ String
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
Status
of the predictor must be ACTIVE
before you can use the
predictor to create a forecast.
#training_parameters ⇒ Hash<String,String>
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.