Amazonka.MachineLearning.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.

Creating a Request

data CreateEvaluation

See: newCreateEvaluation smart constructor.

Constructors

CreateEvaluation'

Fields

• evaluationName :: Maybe Text

  A user-supplied name or description of the Evaluation.

• evaluationId :: Text

  A user-supplied ID that uniquely identifies the Evaluation.

• mLModelId :: Text

  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.

• evaluationDataSourceId :: Text

  The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

Instances

ToJSON CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

toJSON :: CreateEvaluation -> Value

toEncoding :: CreateEvaluation -> Encoding

toJSONList :: [CreateEvaluation] -> Value

toEncodingList :: [CreateEvaluation] -> Encoding

ToHeaders CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

toHeaders :: CreateEvaluation -> [Header]

ToPath CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

toPath :: CreateEvaluation -> ByteString

ToQuery CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

toQuery :: CreateEvaluation -> QueryString

AWSRequest CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Associated Types

type AWSResponse CreateEvaluation

Methods

request :: (Service -> Service) -> CreateEvaluation -> Request CreateEvaluation

response :: MonadResource m => (ByteStringLazy -> IO ByteStringLazy) -> Service -> Proxy CreateEvaluation -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateEvaluation)))

Generic CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Associated Types

type Rep CreateEvaluation :: Type -> Type

Methods

from :: CreateEvaluation -> Rep CreateEvaluation x

to :: Rep CreateEvaluation x -> CreateEvaluation

Read CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

readsPrec :: Int -> ReadS CreateEvaluation

readList :: ReadS [CreateEvaluation]

readPrec :: ReadPrec CreateEvaluation

readListPrec :: ReadPrec [CreateEvaluation]

Show CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

showsPrec :: Int -> CreateEvaluation -> ShowS

show :: CreateEvaluation -> String

showList :: [CreateEvaluation] -> ShowS

NFData CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

rnf :: CreateEvaluation -> ()

Eq CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

(==) :: CreateEvaluation -> CreateEvaluation -> Bool

(/=) :: CreateEvaluation -> CreateEvaluation -> Bool

Hashable CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

hashWithSalt :: Int -> CreateEvaluation -> Int

hash :: CreateEvaluation -> Int

type AWSResponse CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

type AWSResponse CreateEvaluation = CreateEvaluationResponse

type Rep CreateEvaluation

Defined in Amazonka.MachineLearning.CreateEvaluation

type Rep CreateEvaluation = D1 ('MetaData "CreateEvaluation" "Amazonka.MachineLearning.CreateEvaluation" "amazonka-ml-2.0-A3JLJ63WvmfHxGBBIqhdRA" 'False) (C1 ('MetaCons "CreateEvaluation'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "evaluationName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :*: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "evaluationDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))))

newCreateEvaluation

Arguments

:: Text

CreateEvaluation

-> Text

CreateEvaluation

-> Text

CreateEvaluation

-> CreateEvaluation

Create a value of CreateEvaluation with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationName:CreateEvaluation', createEvaluation_evaluationName - A user-supplied name or description of the Evaluation.

CreateEvaluation, createEvaluation_evaluationId - A user-supplied ID that uniquely identifies the Evaluation.

CreateEvaluation, createEvaluation_mLModelId - 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.

CreateEvaluation, createEvaluation_evaluationDataSourceId - The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

Request Lenses

createEvaluation_evaluationName :: Lens' CreateEvaluation (Maybe Text)

A user-supplied name or description of the Evaluation.

createEvaluation_evaluationId :: Lens' CreateEvaluation Text

A user-supplied ID that uniquely identifies the Evaluation.

createEvaluation_mLModelId :: Lens' CreateEvaluation Text

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.

createEvaluation_evaluationDataSourceId :: Lens' CreateEvaluation Text

The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

Destructuring the Response

data CreateEvaluationResponse

Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.

See: newCreateEvaluationResponse smart constructor.

Constructors

CreateEvaluationResponse'

Fields

• evaluationId :: Maybe Text

  The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

• httpStatus :: Int

  The response's http status code.

Instances

Generic CreateEvaluationResponse

Defined in Amazonka.MachineLearning.CreateEvaluation

Associated Types

type Rep CreateEvaluationResponse :: Type -> Type

Methods

from :: CreateEvaluationResponse -> Rep CreateEvaluationResponse x

to :: Rep CreateEvaluationResponse x -> CreateEvaluationResponse

Read CreateEvaluationResponse

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

readsPrec :: Int -> ReadS CreateEvaluationResponse

readList :: ReadS [CreateEvaluationResponse]

readPrec :: ReadPrec CreateEvaluationResponse

readListPrec :: ReadPrec [CreateEvaluationResponse]

Show CreateEvaluationResponse

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

showsPrec :: Int -> CreateEvaluationResponse -> ShowS

show :: CreateEvaluationResponse -> String

showList :: [CreateEvaluationResponse] -> ShowS

NFData CreateEvaluationResponse

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

rnf :: CreateEvaluationResponse -> ()

Eq CreateEvaluationResponse

Defined in Amazonka.MachineLearning.CreateEvaluation

Methods

(==) :: CreateEvaluationResponse -> CreateEvaluationResponse -> Bool

(/=) :: CreateEvaluationResponse -> CreateEvaluationResponse -> Bool

type Rep CreateEvaluationResponse

Defined in Amazonka.MachineLearning.CreateEvaluation

type Rep CreateEvaluationResponse = D1 ('MetaData "CreateEvaluationResponse" "Amazonka.MachineLearning.CreateEvaluation" "amazonka-ml-2.0-A3JLJ63WvmfHxGBBIqhdRA" 'False) (C1 ('MetaCons "CreateEvaluationResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateEvaluationResponse

Arguments

:: Int

$sel:httpStatus:CreateEvaluationResponse'

-> CreateEvaluationResponse

Create a value of CreateEvaluationResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

CreateEvaluation, createEvaluationResponse_evaluationId - The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

$sel:httpStatus:CreateEvaluationResponse', createEvaluationResponse_httpStatus - The response's http status code.

Response Lenses

createEvaluationResponse_evaluationId :: Lens' CreateEvaluationResponse (Maybe Text)

The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

createEvaluationResponse_httpStatus :: Lens' CreateEvaluationResponse Int

The response's http status code.