libZSservicesZSamazonka-mlZSamazonka-ml
Copyright(c) 2013-2021 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone

Amazonka.MachineLearning.CreateEvaluation

Description

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.

Synopsis

Creating a Request

data CreateEvaluation Source #

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

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Eq CreateEvaluation Source # 
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Read CreateEvaluation Source # 
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Show CreateEvaluation Source # 
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Generic CreateEvaluation Source # 
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Associated Types

type Rep CreateEvaluation :: Type -> Type #

NFData CreateEvaluation Source # 
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Methods

rnf :: CreateEvaluation -> () #

Hashable CreateEvaluation Source # 
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ToJSON CreateEvaluation Source # 
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AWSRequest CreateEvaluation Source # 
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Associated Types

type AWSResponse CreateEvaluation #

ToHeaders CreateEvaluation Source # 
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ToPath CreateEvaluation Source # 
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ToQuery CreateEvaluation Source # 
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type Rep CreateEvaluation Source # 
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type Rep CreateEvaluation = D1 ('MetaData "CreateEvaluation" "Amazonka.MachineLearning.CreateEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" '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))))
type AWSResponse CreateEvaluation Source # 
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newCreateEvaluation Source #

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.

$sel:evaluationId:CreateEvaluation', createEvaluation_evaluationId - A user-supplied ID that uniquely identifies the Evaluation.

$sel:mLModelId: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.

$sel:evaluationDataSourceId: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) Source #

A user-supplied name or description of the Evaluation.

createEvaluation_evaluationId :: Lens' CreateEvaluation Text Source #

A user-supplied ID that uniquely identifies the Evaluation.

createEvaluation_mLModelId :: Lens' CreateEvaluation Text Source #

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 Source #

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 Source #

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

Instances details
Eq CreateEvaluationResponse Source # 
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Read CreateEvaluationResponse Source # 
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Show CreateEvaluationResponse Source # 
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Generic CreateEvaluationResponse Source # 
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Associated Types

type Rep CreateEvaluationResponse :: Type -> Type #

NFData CreateEvaluationResponse Source # 
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type Rep CreateEvaluationResponse Source # 
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type Rep CreateEvaluationResponse = D1 ('MetaData "CreateEvaluationResponse" "Amazonka.MachineLearning.CreateEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" '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 Source #

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:

$sel:evaluationId: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) Source #

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