Class: Aws::MachineLearning::Types::Evaluation

Inherits:

Struct

• Object
• Struct
• Aws::MachineLearning::Types::Evaluation

Defined in:

(unknown)

Overview

Represents the output of GetEvaluation operation.

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

Instance Attribute Summary

• #compute_time ⇒ Integer

  Long integer type that is a 64-bit signed number.

  .

• #created_at ⇒ Time

  The time that the Evaluation was created.

• #created_by_iam_user ⇒ String

  The AWS user account that invoked the evaluation.

• #evaluation_data_source_id ⇒ String

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

• #evaluation_id ⇒ String

  The ID that is assigned to the Evaluation at creation.

• #finished_at ⇒ Time

  A timestamp represented in epoch time.

  .

• #input_data_location_s3 ⇒ String

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

• #last_updated_at ⇒ Time

  The time of the most recent edit to the Evaluation.

• #message ⇒ String

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

• #ml_model_id ⇒ String

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

• #name ⇒ String

  A user-supplied name or description of the Evaluation.

• #performance_metrics ⇒ Types::PerformanceMetrics

  Measurements of how well the MLModel performed, using observations referenced by the DataSource.

• #started_at ⇒ Time

  A timestamp represented in epoch time.

  .

• #status ⇒ String

  The status of the evaluation.

Instance Attribute Details

#compute_time ⇒ Integer

Long integer type that is a 64-bit signed number.

Returns:

• (Integer) —

  Long integer type that is a 64-bit signed number.

  .

#created_at ⇒ Time

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

Returns:

• (Time) —

  The time that the Evaluation was created.

#created_by_iam_user ⇒ String

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.

Returns:

• (String) —

  The AWS user account that invoked the evaluation.

#evaluation_data_source_id ⇒ String

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

Returns:

• (String) —

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

#evaluation_id ⇒ String

The ID that is assigned to the Evaluation at creation.

Returns:

• (String) —

  The ID that is assigned to the Evaluation at creation.

#finished_at ⇒ Time

A timestamp represented in epoch time.

Returns:

• (Time) —

  A timestamp represented in epoch time.

  .

#input_data_location_s3 ⇒ String

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

Returns:

• (String) —

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

#last_updated_at ⇒ Time

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

Returns:

• (Time) —

  The time of the most recent edit to the Evaluation.

#message ⇒ String

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

Returns:

• (String) —

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

#ml_model_id ⇒ String

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

Returns:

• (String) —

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

#name ⇒ String

A user-supplied name or description of the Evaluation.

Returns:

• (String) —

  A user-supplied name or description of the Evaluation.

#performance_metrics ⇒ Types::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.

Returns:

• (Types::PerformanceMetrics) —

  Measurements of how well the MLModel performed, using observations referenced by the DataSource.

#started_at ⇒ Time

A timestamp represented in epoch time.

Returns:

• (Time) —

  A timestamp represented in epoch time.

  .

#status ⇒ String

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.

  Possible values:

  • PENDING
  • INPROGRESS
  • FAILED
  • COMPLETED
  • DELETED

Returns:

• (String) —

  The status of the evaluation.