class Aws::MachineLearning::Types::GetEvaluationOutput
Represents the output of a `GetEvaluation` operation and describes an `Evaluation`.
@!attribute [rw] evaluation_id
The evaluation ID which is same as the `EvaluationId` in the request. @return [String]
@!attribute [rw] ml_model_id
The ID of the `MLModel` that was the focus of the evaluation. @return [String]
@!attribute [rw] evaluation_data_source_id
The `DataSource` used for this evaluation. @return [String]
@!attribute [rw] input_data_location_s3
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). @return [String]
@!attribute [rw] created_by_iam_user
The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. @return [String]
@!attribute [rw] created_at
The time that the `Evaluation` was created. The time is expressed in epoch time. @return [Time]
@!attribute [rw] last_updated_at
The time of the most recent edit to the `Evaluation`. The time is expressed in epoch time. @return [Time]
@!attribute [rw] name
A user-supplied name or description of the `Evaluation`. @return [String]
@!attribute [rw] status
The status of the evaluation. This element can have one of the following values: * `PENDING` - Amazon Machine Language (Amazon ML) submitted a request to evaluate an `MLModel`. * `INPROGRESS` - The evaluation is underway. * `FAILED` - The request to evaluate an `MLModel` did not run to completion. It is not usable. * `COMPLETED` - The evaluation process completed successfully. * `DELETED` - The `Evaluation` is marked as deleted. It is not usable. @return [String]
@!attribute [rw] performance_metrics
Measurements of how well the `MLModel` performed using observations referenced by the `DataSource`. One of the following metric is returned based on the type of the `MLModel`\: * BinaryAUC: A binary `MLModel` uses the Area Under the Curve (AUC) technique to measure performance. * RegressionRMSE: A regression `MLModel` uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. * MulticlassAvgFScore: A multiclass `MLModel` uses the F1 score technique to measure performance. For more information about performance metrics, please see the [Amazon Machine Learning Developer Guide][1]. [1]: https://docs.aws.amazon.com/machine-learning/latest/dg @return [Types::PerformanceMetrics]
@!attribute [rw] log_uri
A link to the file that contains logs of the `CreateEvaluation` operation. @return [String]
@!attribute [rw] message
A description of the most recent details about evaluating the `MLModel`. @return [String]
@!attribute [rw] compute_time
The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the `Evaluation`, normalized and scaled on computation resources. `ComputeTime` is only available if the `Evaluation` is in the `COMPLETED` state. @return [Integer]
@!attribute [rw] finished_at
The epoch time when Amazon Machine Learning marked the `Evaluation` as `COMPLETED` or `FAILED`. `FinishedAt` is only available when the `Evaluation` is in the `COMPLETED` or `FAILED` state. @return [Time]
@!attribute [rw] started_at
The epoch time when Amazon Machine Learning marked the `Evaluation` as `INPROGRESS`. `StartedAt` isn't available if the `Evaluation` is in the `PENDING` state. @return [Time]
Constants
- SENSITIVE