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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