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Evaluation of an MLModel. An MLModel
is evaluated on a set of observations associated to a DataSource. Like
a DataSource for an MLModel, the DataSource
for an Evaluation contains values for the Target Variable. The Evaluation
compares the predicted result for each observation to the actual outcome and provides
a summary so that you know how effective the MLModel functions on the
test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE
or MulticlassAvgFScore based on the corresponding MLModelType: BINARY,
REGRESSION or MULTICLASS.
CreateEvaluation is an asynchronous operation. In response to CreateEvaluation,
Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status
to PENDING. After the Evaluation is created and ready for
use, Amazon ML sets the status to COMPLETED.
You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
Namespace: Amazon.MachineLearning.Model
Assembly: AWSSDK.dll
Version: (assembly version)
public class CreateEvaluationRequest : AmazonMachineLearningRequest IRequestEvents
The CreateEvaluationRequest type exposes the following members
| Name | Description | |
|---|---|---|
|
CreateEvaluationRequest() |
| Name | Type | Description | |
|---|---|---|---|
|
EvaluationDataSourceId | System.String |
Gets and sets the property EvaluationDataSourceId.
The ID of the |
|
EvaluationId | System.String |
Gets and sets the property EvaluationId.
A user-supplied ID that uniquely identifies the |
|
EvaluationName | System.String |
Gets and sets the property EvaluationName.
A user-supplied name or description of the |
|
MLModelId | System.String |
Gets and sets the property MLModelId.
The ID of the
The schema used in creating the |
.NET Framework:
Supported in: 4.5, 4.0, 3.5