Struct aws_sdk_machinelearning::client::fluent_builders::CreateEvaluation

pub struct CreateEvaluation<C = DynConnector, M = DefaultMiddleware, R = Standard> { /* fields omitted */ }

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

impl<C, M, R> CreateEvaluation<C, M, R> where
    C: SmithyConnector,
    M: SmithyMiddleware<C>,
    R: NewRequestPolicy, 

pub async fn send(
    self
) -> Result<CreateEvaluationOutput, SdkError<CreateEvaluationError>> where
    R::Policy: SmithyRetryPolicy<CreateEvaluationInputOperationOutputAlias, CreateEvaluationOutput, CreateEvaluationError, CreateEvaluationInputOperationRetryAlias>, 

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.

pub fn evaluation_id(self, input: impl Into<String>) -> Self

A user-supplied ID that uniquely identifies the Evaluation.

pub fn set_evaluation_id(self, input: Option<String>) -> Self

A user-supplied ID that uniquely identifies the Evaluation.

pub fn evaluation_name(self, input: impl Into<String>) -> Self

A user-supplied name or description of the Evaluation.

pub fn set_evaluation_name(self, input: Option<String>) -> Self

A user-supplied name or description of the Evaluation.

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.

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.

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.

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.

Trait Implementations

impl<C: Clone, M: Clone, R: Clone> Clone for CreateEvaluation<C, M, R>

fn clone(&self) -> CreateEvaluation<C, M, R>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<C: Debug, M: Debug, R: Debug> Debug for CreateEvaluation<C, M, R>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.