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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
Assembly: AWSSDK.dll
Version: (assembly version)
public abstract CreateEvaluationResponse CreateEvaluation( CreateEvaluationRequest request )
Container for the necessary parameters to execute the CreateEvaluation service method.
| Exception | Condition |
|---|---|
| IdempotentParameterMismatchException | A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request. |
| InternalServerException | An error on the server occurred when trying to process a request. |
| InvalidInputException | An error on the client occurred. Typically, the cause is an invalid input value. |
.NET Framework:
Supported in: 4.5, 4.0, 3.5