Values.EvaluationSourceRepresents the output of GetEvaluation operation. The content consists of the detailed metadata and data file information and the current status of the Evaluation.
type nonrec t = {evaluationId : EntityId.t option;The ID that is assigned to the Evaluation at creation.
*)mLModelId : EntityId.t option;The ID of the MLModel that is the focus of the evaluation.
*)evaluationDataSourceId : EntityId.t option;The ID of the DataSource that is used to evaluate the MLModel.
*)inputDataLocationS3 : S3Url.t option;The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
*)createdByIamUser : AwsUserArn.t option;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.
*)createdAt : EpochTime.t option;The time that the Evaluation was created. The time is expressed in epoch time.
*)lastUpdatedAt : EpochTime.t option;The time of the most recent edit to the Evaluation. The time is expressed in epoch time.
*)name : EntityName.t option;A user-supplied name or description of the Evaluation.
*)status : EntityStatus.t option;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.
*)performanceMetrics : PerformanceMetrics.t option;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.
*)message : Message.t option;A description of the most recent details about evaluating the MLModel.
*)computeTime : LongType.t option;finishedAt : EpochTime.t option;startedAt : EpochTime.t option;}val make :
?evaluationId:??? ->
?mLModelId:??? ->
?evaluationDataSourceId:??? ->
?inputDataLocationS3:??? ->
?createdByIamUser:??? ->
?createdAt:??? ->
?lastUpdatedAt:??? ->
?name:??? ->
?status:??? ->
?performanceMetrics:??? ->
?message:??? ->
?computeTime:??? ->
?finishedAt:??? ->
?startedAt:??? ->
unit ->
tval to_value :
t ->
[> `Structure of
(string
* [> `Enum of string
| `Long of LongType.t
| `String of EntityId.t
| `Structure of
(string
* [> `Map of
([> `String of PerformanceMetricsPropertyKey.t ]
* [> `String of PerformanceMetricsPropertyValue.t ])
list ])
list
| `Timestamp of EpochTime.t ])
list ]