Class CreateMLModelRequest
- All Implemented Interfaces:
ReadLimitInfo
,Serializable
,Cloneable
- See Also:
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Field Summary
Fields inherited from class com.amazonaws.AmazonWebServiceRequest
NOOP
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionaddParametersEntry
(String key, String value) Removes all the entries added into Parameters.clone()
Creates a shallow clone of this request.boolean
A user-supplied ID that uniquely identifies theMLModel
.A user-supplied name or description of theMLModel
.The category of supervised learning that thisMLModel
will address.A list of the training parameters in theMLModel
.The data recipe for creatingMLModel
.The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe.TheDataSource
that points to the training data.int
hashCode()
void
setMLModelId
(String mLModelId) A user-supplied ID that uniquely identifies theMLModel
.void
setMLModelName
(String mLModelName) A user-supplied name or description of theMLModel
.void
setMLModelType
(MLModelType mLModelType) The category of supervised learning that thisMLModel
will address.void
setMLModelType
(String mLModelType) The category of supervised learning that thisMLModel
will address.void
setParameters
(Map<String, String> parameters) A list of the training parameters in theMLModel
.void
The data recipe for creatingMLModel
.void
setRecipeUri
(String recipeUri) The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe.void
setTrainingDataSourceId
(String trainingDataSourceId) TheDataSource
that points to the training data.toString()
Returns a string representation of this object; useful for testing and debugging.withMLModelId
(String mLModelId) A user-supplied ID that uniquely identifies theMLModel
.withMLModelName
(String mLModelName) A user-supplied name or description of theMLModel
.withMLModelType
(MLModelType mLModelType) The category of supervised learning that thisMLModel
will address.withMLModelType
(String mLModelType) The category of supervised learning that thisMLModel
will address.withParameters
(Map<String, String> parameters) A list of the training parameters in theMLModel
.withRecipe
(String recipe) The data recipe for creatingMLModel
.withRecipeUri
(String recipeUri) The Amazon Simple Storage Service (Amazon S3) location and file name that contains theMLModel
recipe.withTrainingDataSourceId
(String trainingDataSourceId) TheDataSource
that points to the training data.Methods inherited from class com.amazonaws.AmazonWebServiceRequest
copyBaseTo, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout
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Constructor Details
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CreateMLModelRequest
public CreateMLModelRequest()
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Method Details
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setMLModelId
A user-supplied ID that uniquely identifies the
MLModel
.- Parameters:
mLModelId
- A user-supplied ID that uniquely identifies theMLModel
.
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getMLModelId
A user-supplied ID that uniquely identifies the
MLModel
.- Returns:
- A user-supplied ID that uniquely identifies the
MLModel
.
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withMLModelId
A user-supplied ID that uniquely identifies the
MLModel
.- Parameters:
mLModelId
- A user-supplied ID that uniquely identifies theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setMLModelName
A user-supplied name or description of the
MLModel
.- Parameters:
mLModelName
- A user-supplied name or description of theMLModel
.
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getMLModelName
A user-supplied name or description of the
MLModel
.- Returns:
- A user-supplied name or description of the
MLModel
.
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withMLModelName
A user-supplied name or description of the
MLModel
.- Parameters:
mLModelName
- A user-supplied name or description of theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setMLModelType
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- See Also:
- Choose
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getMLModelType
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Returns:
- The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- See Also:
- Choose
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withMLModelType
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
- Choose
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setMLModelType
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- See Also:
- Choose
-
withMLModelType
The category of supervised learning that this
MLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Parameters:
mLModelType
- The category of supervised learning that thisMLModel
will address. Choose from the following types:- Choose
REGRESSION
if theMLModel
will be used to predict a numeric value. - Choose
BINARY
if theMLModel
result has two possible values. - Choose
MULTICLASS
if theMLModel
result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
- Choose
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
- Choose
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getParameters
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 theMLModel
. 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.
- Returns:
- 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 theMLModel
. 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.
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setParameters
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 theMLModel
. 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.
- Parameters:
parameters
- 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.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 theMLModel
. 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.
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withParameters
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 theMLModel
. 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.
- Parameters:
parameters
- 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.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 theMLModel
. 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.
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- Returns:
- Returns a reference to this object so that method calls can be chained together.
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addParametersEntry
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clearParametersEntries
Removes all the entries added into Parameters. <p> Returns a reference to this object so that method calls can be chained together. -
setTrainingDataSourceId
The
DataSource
that points to the training data.- Parameters:
trainingDataSourceId
- TheDataSource
that points to the training data.
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getTrainingDataSourceId
The
DataSource
that points to the training data.- Returns:
- The
DataSource
that points to the training data.
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withTrainingDataSourceId
The
DataSource
that points to the training data.- Parameters:
trainingDataSourceId
- TheDataSource
that points to the training data.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setRecipe
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.- Parameters:
recipe
- The data recipe for creatingMLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
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getRecipe
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.- Returns:
- 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.
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withRecipe
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.- Parameters:
recipe
- The data recipe for creatingMLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setRecipeUri
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.- Parameters:
recipeUri
- 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.
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getRecipeUri
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.- Returns:
- 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.
-
withRecipeUri
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.- Parameters:
recipeUri
- 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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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toString
Returns a string representation of this object; useful for testing and debugging. -
equals
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hashCode
public int hashCode() -
clone
Description copied from class:AmazonWebServiceRequest
Creates a shallow clone of this request. Explicitly does not clone the deep structure of the request object.- Overrides:
clone
in classAmazonWebServiceRequest
- See Also:
-