pub struct CreateEvaluationFluentBuilder { /* private fields */ }Expand description
Fluent builder constructing a request to CreateEvaluation.
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.
Implementations§
Source§impl CreateEvaluationFluentBuilder
impl CreateEvaluationFluentBuilder
Sourcepub fn as_input(&self) -> &CreateEvaluationInputBuilder
pub fn as_input(&self) -> &CreateEvaluationInputBuilder
Access the CreateEvaluation as a reference.
Sourcepub async fn send(
self,
) -> Result<CreateEvaluationOutput, SdkError<CreateEvaluationError, HttpResponse>>
pub async fn send( self, ) -> Result<CreateEvaluationOutput, SdkError<CreateEvaluationError, HttpResponse>>
Sends the request and returns the response.
If an error occurs, an SdkError will be returned with additional details that
can be matched against.
By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.
Sourcepub fn customize(
self,
) -> CustomizableOperation<CreateEvaluationOutput, CreateEvaluationError, Self>
pub fn customize( self, ) -> CustomizableOperation<CreateEvaluationOutput, CreateEvaluationError, Self>
Consumes this builder, creating a customizable operation that can be modified before being sent.
Sourcepub fn evaluation_id(self, input: impl Into<String>) -> Self
pub fn evaluation_id(self, input: impl Into<String>) -> Self
A user-supplied ID that uniquely identifies the Evaluation.
Sourcepub fn set_evaluation_id(self, input: Option<String>) -> Self
pub fn set_evaluation_id(self, input: Option<String>) -> Self
A user-supplied ID that uniquely identifies the Evaluation.
Sourcepub fn get_evaluation_id(&self) -> &Option<String>
pub fn get_evaluation_id(&self) -> &Option<String>
A user-supplied ID that uniquely identifies the Evaluation.
Sourcepub fn evaluation_name(self, input: impl Into<String>) -> Self
pub fn evaluation_name(self, input: impl Into<String>) -> Self
A user-supplied name or description of the Evaluation.
Sourcepub fn set_evaluation_name(self, input: Option<String>) -> Self
pub fn set_evaluation_name(self, input: Option<String>) -> Self
A user-supplied name or description of the Evaluation.
Sourcepub fn get_evaluation_name(&self) -> &Option<String>
pub fn get_evaluation_name(&self) -> &Option<String>
A user-supplied name or description of the Evaluation.
Sourcepub fn ml_model_id(self, input: impl Into<String>) -> Self
pub fn ml_model_id(self, input: impl Into<String>) -> Self
The ID of the MLModel to evaluate.
The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation.
Sourcepub fn set_ml_model_id(self, input: Option<String>) -> Self
pub fn set_ml_model_id(self, input: Option<String>) -> Self
The ID of the MLModel to evaluate.
The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation.
Sourcepub fn get_ml_model_id(&self) -> &Option<String>
pub fn get_ml_model_id(&self) -> &Option<String>
The ID of the MLModel to evaluate.
The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation.
Sourcepub fn evaluation_data_source_id(self, input: impl Into<String>) -> Self
pub fn evaluation_data_source_id(self, input: impl Into<String>) -> Self
The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.
Sourcepub fn set_evaluation_data_source_id(self, input: Option<String>) -> Self
pub fn set_evaluation_data_source_id(self, input: Option<String>) -> Self
The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.
Sourcepub fn get_evaluation_data_source_id(&self) -> &Option<String>
pub fn get_evaluation_data_source_id(&self) -> &Option<String>
The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.
Trait Implementations§
Source§impl Clone for CreateEvaluationFluentBuilder
impl Clone for CreateEvaluationFluentBuilder
Source§fn clone(&self) -> CreateEvaluationFluentBuilder
fn clone(&self) -> CreateEvaluationFluentBuilder
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreAuto Trait Implementations§
impl Freeze for CreateEvaluationFluentBuilder
impl !RefUnwindSafe for CreateEvaluationFluentBuilder
impl Send for CreateEvaluationFluentBuilder
impl Sync for CreateEvaluationFluentBuilder
impl Unpin for CreateEvaluationFluentBuilder
impl !UnwindSafe for CreateEvaluationFluentBuilder
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