Creates a new Evaluation of an MLModel
Creates a new 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.
create_evaluation is an
asynchronous operation. In response to
create_evaluation, 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 get_evaluation
operation to check progress of the evaluation during the creation
operation.
machinelearning_create_evaluation(EvaluationId, EvaluationName, MLModelId, EvaluationDataSourceId)
EvaluationId |
[required] A user-supplied ID that uniquely identifies the |
EvaluationName |
A user-supplied name or description of the |
MLModelId |
[required] The ID of the The schema used in creating the |
EvaluationDataSourceId |
[required] The ID of the |
A list with the following syntax:
list( EvaluationId = "string" )
svc$create_evaluation( EvaluationId = "string", EvaluationName = "string", MLModelId = "string", EvaluationDataSourceId = "string" )
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