amazonka-ml-1.3.4: Amazon Machine Learning SDK.

Copyright(c) 2013-2015 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone
LanguageHaskell2010

Network.AWS.MachineLearning.CreateMLModel

Contents

Description

Creates a new MLModel using the data files and the recipe as information sources.

An MLModel is nearly immutable. Users can only update the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

See: AWS API Reference for CreateMLModel.

Synopsis

Creating a Request

createMLModel Source

Creates a value of CreateMLModel with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

Request Lenses

cmlmRecipe :: Lens' CreateMLModel (Maybe Text) Source

The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.

cmlmRecipeURI :: Lens' CreateMLModel (Maybe Text) Source

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.

cmlmMLModelName :: Lens' CreateMLModel (Maybe Text) Source

A user-supplied name or description of the MLModel.

cmlmParameters :: Lens' CreateMLModel (HashMap Text Text) Source

A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.

The following is the current set of training parameters:

  • 'sgd.l1RegularizationAmount' - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • 'sgd.l2RegularizationAmount' - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value such as 1.0E-08.

    The valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • 'sgd.maxPasses' - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
  • 'sgd.maxMLModelSizeInBytes' - Maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

cmlmMLModelId :: Lens' CreateMLModel Text Source

A user-supplied ID that uniquely identifies the MLModel.

cmlmMLModelType :: Lens' CreateMLModel MLModelType Source

The category of supervised learning that this MLModel will address. Choose from the following types:

  • Choose REGRESSION if the MLModel will be used to predict a numeric value.
  • Choose BINARY if the MLModel result has two possible values.
  • Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

cmlmTrainingDataSourceId :: Lens' CreateMLModel Text Source

The DataSource that points to the training data.

Destructuring the Response

createMLModelResponse Source

Creates a value of CreateMLModelResponse with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

data CreateMLModelResponse Source

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

The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

See: createMLModelResponse smart constructor.

Response Lenses

cmlmrsMLModelId :: Lens' CreateMLModelResponse (Maybe Text) Source

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.