// 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. 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.
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. 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.
// 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 . 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 .
func (c *Client) CreateEvaluation(ctx context.Context, params *CreateEvaluationInput, optFns ...func(*Options)) (*CreateEvaluationOutput, error) {
	if params == nil {
		params = &CreateEvaluationInput{}
	}
	result, metadata, err := c.invokeOperation(ctx, "CreateEvaluation", params, optFns, addOperationCreateEvaluationMiddlewares)
	if err != nil {
		return nil, err
	}
	out := result.(*CreateEvaluationOutput)
	out.ResultMetadata = metadata
	return out, nil
}
func ( c * Client ) CreateEvaluation ( ctx context . Context , params * CreateEvaluationInput , optFns ... func ( * Options ) ) ( * CreateEvaluationOutput , error ) { if params == nil { params = & CreateEvaluationInput { } } result , metadata , err := c . invokeOperation ( ctx , " " , params , optFns , addOperationCreateEvaluationMiddlewares ) if err != nil { return nil , err } out := result . ( * CreateEvaluationOutput ) out . ResultMetadata = metadata return out , nil }
Creates a new Evaluation of an MLModel.
Creates a new Evaluation of an MLModel .