class Aws::MachineLearning::Types::Evaluation

Represents the output of `GetEvaluation` operation.

The content consists of the detailed metadata and data file information and the current status of the `Evaluation`.

@!attribute [rw] evaluation_id

The ID that is assigned to the `Evaluation` at creation.
@return [String]

@!attribute [rw] ml_model_id

The ID of the `MLModel` that is the focus of the evaluation.
@return [String]

@!attribute [rw] evaluation_data_source_id

The ID of the `DataSource` that is used to evaluate the `MLModel`.
@return [String]

@!attribute [rw] input_data_location_s3

The location and name of the data in Amazon Simple Storage Server
(Amazon S3) that is used in the evaluation.
@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 Learning (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 metrics 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] message

A description of the most recent details about evaluating the
`MLModel`.
@return [String]

@!attribute [rw] compute_time

Long integer type that is a 64-bit signed number.
@return [Integer]

@!attribute [rw] finished_at

A timestamp represented in epoch time.
@return [Time]

@!attribute [rw] started_at

A timestamp represented in epoch time.
@return [Time]

Constants

SENSITIVE