Documentation

Represents the output of GetEvaluation operation.

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

See: newEvaluation smart constructor.

Create a value of Evaluation 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:

$sel:status:Evaluation', evaluation_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.

$sel:performanceMetrics:Evaluation', evaluation_performanceMetrics - 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.

$sel:lastUpdatedAt:Evaluation', evaluation_lastUpdatedAt - The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

$sel:createdAt:Evaluation', evaluation_createdAt - The time that the Evaluation was created. The time is expressed in epoch time.

$sel:computeTime:Evaluation', evaluation_computeTime - Undocumented member.

$sel:inputDataLocationS3:Evaluation', evaluation_inputDataLocationS3 - The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

$sel:mLModelId:Evaluation', evaluation_mLModelId - The ID of the MLModel that is the focus of the evaluation.

$sel:startedAt:Evaluation', evaluation_startedAt - Undocumented member.

$sel:finishedAt:Evaluation', evaluation_finishedAt - Undocumented member.

$sel:createdByIamUser:Evaluation', evaluation_createdByIamUser - 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.

$sel:name:Evaluation', evaluation_name - A user-supplied name or description of the Evaluation.

$sel:evaluationId:Evaluation', evaluation_evaluationId - The ID that is assigned to the Evaluation at creation.

$sel:message:Evaluation', evaluation_message - A description of the most recent details about evaluating the MLModel.

$sel:evaluationDataSourceId:Evaluation', evaluation_evaluationDataSourceId - The ID of the DataSource that is used to evaluate the MLModel.

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.

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.

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

The time that the Evaluation was created. The time is expressed in epoch time.

Undocumented member.

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

The ID of the MLModel that is the focus of the evaluation.

Undocumented member.

Undocumented member.

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.

A user-supplied name or description of the Evaluation.

The ID that is assigned to the Evaluation at creation.

A description of the most recent details about evaluating the MLModel.

The ID of the DataSource that is used to evaluate the MLModel.