Copyright | (c) 2013-2018 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay <brendan.g.hay+amazonka@gmail.com> |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | None |
Language | Haskell2010 |
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only 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
has been created and ready is for use, Amazon ML sets the status to COMPLETED
.
You can use the GetMLModel
operation to check the 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.
- createMLModel :: Text -> MLModelType -> Text -> CreateMLModel
- data CreateMLModel
- cmlmRecipe :: Lens' CreateMLModel (Maybe Text)
- cmlmRecipeURI :: Lens' CreateMLModel (Maybe Text)
- cmlmMLModelName :: Lens' CreateMLModel (Maybe Text)
- cmlmParameters :: Lens' CreateMLModel (HashMap Text Text)
- cmlmMLModelId :: Lens' CreateMLModel Text
- cmlmMLModelType :: Lens' CreateMLModel MLModelType
- cmlmTrainingDataSourceId :: Lens' CreateMLModel Text
- createMLModelResponse :: Int -> CreateMLModelResponse
- data CreateMLModelResponse
- cmlmrsMLModelId :: Lens' CreateMLModelResponse (Maybe Text)
- cmlmrsResponseStatus :: Lens' CreateMLModelResponse Int
Creating a Request
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:
cmlmRecipe
- The data recipe for creating theMLModel
. 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
- The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
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
- A user-supplied name or description of theMLModel
.cmlmParameters
- A list of the training parameters in theMLModel
. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: *sgd.maxMLModelSizeInBytes
- The 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 from100000
to2147483648
. The default value is33554432
. *sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. *sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values areauto
andnone
. The default value isnone
. We strongly recommend that you shuffle your data. *sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as1.0E-08
. The value is a double that ranges from0
toMAX_DOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. *sgd.l2RegularizationAmount
- The 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 as1.0E-08
. The value is a double that ranges from0
toMAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.cmlmMLModelId
- A user-supplied ID that uniquely identifies theMLModel
.cmlmMLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types: * ChooseREGRESSION
if theMLModel
will be used to predict a numeric value. * ChooseBINARY
if theMLModel
result has two possible values. * ChooseMULTICLASS
if theMLModel
result has a limited number of values. For more information, see the Amazon Machine Learning Developer Guide .cmlmTrainingDataSourceId
- TheDataSource
that points to the training data.
data CreateMLModel Source #
See: createMLModel
smart constructor.
Request Lenses
cmlmRecipe :: Lens' CreateMLModel (Maybe Text) Source #
The data recipe for creating the 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.maxMLModelSizeInBytes
- The 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
. * sgd.maxPasses
- The 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.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto
and none
. The default value is none
. We strongly recommend that you shuffle your data. * sgd.l1RegularizationAmount
- The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a 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 to not use L1 normalization. This parameter can't be used when L2
is specified. Use this parameter sparingly. * sgd.l2RegularizationAmount
- The 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 value is a double that ranges from 0
to MAX_DOUBLE
. The default is to not use L2 normalization. This parameter can't be used when L1
is specified. Use this parameter sparingly.
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:
cmlmrsMLModelId
- A user-supplied ID that uniquely identifies theMLModel
. This value should be identical to the value of theMLModelId
in the request.cmlmrsResponseStatus
- -- | The response status code.
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.
cmlmrsResponseStatus :: Lens' CreateMLModelResponse Int Source #
- - | The response status code.