AWS SDK for C++  1.9.16
AWS SDK for C++
Public Member Functions | List of all members
Aws::MachineLearning::Model::CreateMLModelRequest Class Reference

#include <CreateMLModelRequest.h>

+ Inheritance diagram for Aws::MachineLearning::Model::CreateMLModelRequest:

Public Member Functions

 CreateMLModelRequest ()
 
virtual const char * GetServiceRequestName () const override
 
Aws::String SerializePayload () const override
 
Aws::Http::HeaderValueCollection GetRequestSpecificHeaders () const override
 
const Aws::StringGetMLModelId () const
 
bool MLModelIdHasBeenSet () const
 
void SetMLModelId (const Aws::String &value)
 
void SetMLModelId (Aws::String &&value)
 
void SetMLModelId (const char *value)
 
CreateMLModelRequestWithMLModelId (const Aws::String &value)
 
CreateMLModelRequestWithMLModelId (Aws::String &&value)
 
CreateMLModelRequestWithMLModelId (const char *value)
 
const Aws::StringGetMLModelName () const
 
bool MLModelNameHasBeenSet () const
 
void SetMLModelName (const Aws::String &value)
 
void SetMLModelName (Aws::String &&value)
 
void SetMLModelName (const char *value)
 
CreateMLModelRequestWithMLModelName (const Aws::String &value)
 
CreateMLModelRequestWithMLModelName (Aws::String &&value)
 
CreateMLModelRequestWithMLModelName (const char *value)
 
const MLModelTypeGetMLModelType () const
 
bool MLModelTypeHasBeenSet () const
 
void SetMLModelType (const MLModelType &value)
 
void SetMLModelType (MLModelType &&value)
 
CreateMLModelRequestWithMLModelType (const MLModelType &value)
 
CreateMLModelRequestWithMLModelType (MLModelType &&value)
 
const Aws::Map< Aws::String, Aws::String > & GetParameters () const
 
bool ParametersHasBeenSet () const
 
void SetParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
CreateMLModelRequestWithParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
CreateMLModelRequestWithParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
CreateMLModelRequestAddParameters (const Aws::String &key, const Aws::String &value)
 
CreateMLModelRequestAddParameters (Aws::String &&key, const Aws::String &value)
 
CreateMLModelRequestAddParameters (const Aws::String &key, Aws::String &&value)
 
CreateMLModelRequestAddParameters (Aws::String &&key, Aws::String &&value)
 
CreateMLModelRequestAddParameters (const char *key, Aws::String &&value)
 
CreateMLModelRequestAddParameters (Aws::String &&key, const char *value)
 
CreateMLModelRequestAddParameters (const char *key, const char *value)
 
const Aws::StringGetTrainingDataSourceId () const
 
bool TrainingDataSourceIdHasBeenSet () const
 
void SetTrainingDataSourceId (const Aws::String &value)
 
void SetTrainingDataSourceId (Aws::String &&value)
 
void SetTrainingDataSourceId (const char *value)
 
CreateMLModelRequestWithTrainingDataSourceId (const Aws::String &value)
 
CreateMLModelRequestWithTrainingDataSourceId (Aws::String &&value)
 
CreateMLModelRequestWithTrainingDataSourceId (const char *value)
 
const Aws::StringGetRecipe () const
 
bool RecipeHasBeenSet () const
 
void SetRecipe (const Aws::String &value)
 
void SetRecipe (Aws::String &&value)
 
void SetRecipe (const char *value)
 
CreateMLModelRequestWithRecipe (const Aws::String &value)
 
CreateMLModelRequestWithRecipe (Aws::String &&value)
 
CreateMLModelRequestWithRecipe (const char *value)
 
const Aws::StringGetRecipeUri () const
 
bool RecipeUriHasBeenSet () const
 
void SetRecipeUri (const Aws::String &value)
 
void SetRecipeUri (Aws::String &&value)
 
void SetRecipeUri (const char *value)
 
CreateMLModelRequestWithRecipeUri (const Aws::String &value)
 
CreateMLModelRequestWithRecipeUri (Aws::String &&value)
 
CreateMLModelRequestWithRecipeUri (const char *value)
 
- Public Member Functions inherited from Aws::MachineLearning::MachineLearningRequest
virtual ~MachineLearningRequest ()
 
void AddParametersToRequest (Aws::Http::HttpRequest &httpRequest) const
 
Aws::Http::HeaderValueCollection GetHeaders () const override
 
- Public Member Functions inherited from Aws::AmazonSerializableWebServiceRequest
 AmazonSerializableWebServiceRequest ()
 
virtual ~AmazonSerializableWebServiceRequest ()
 
std::shared_ptr< Aws::IOStreamGetBody () const override
 
- Public Member Functions inherited from Aws::AmazonWebServiceRequest
 AmazonWebServiceRequest ()
 
virtual ~AmazonWebServiceRequest ()=default
 
virtual void AddQueryStringParameters (Aws::Http::URI &uri) const
 
virtual void PutToPresignedUrl (Aws::Http::URI &uri) const
 
virtual bool IsStreaming () const
 
virtual bool IsEventStreamRequest () const
 
virtual bool SignBody () const
 
virtual bool IsChunked () const
 
virtual void SetRequestSignedHandler (const RequestSignedHandler &handler)
 
virtual const RequestSignedHandlerGetRequestSignedHandler () const
 
const Aws::IOStreamFactoryGetResponseStreamFactory () const
 
void SetResponseStreamFactory (const Aws::IOStreamFactory &factory)
 
virtual void SetDataReceivedEventHandler (const Aws::Http::DataReceivedEventHandler &dataReceivedEventHandler)
 
virtual void SetDataSentEventHandler (const Aws::Http::DataSentEventHandler &dataSentEventHandler)
 
virtual void SetContinueRequestHandler (const Aws::Http::ContinueRequestHandler &continueRequestHandler)
 
virtual void SetDataReceivedEventHandler (Aws::Http::DataReceivedEventHandler &&dataReceivedEventHandler)
 
virtual void SetDataSentEventHandler (Aws::Http::DataSentEventHandler &&dataSentEventHandler)
 
virtual void SetContinueRequestHandler (Aws::Http::ContinueRequestHandler &&continueRequestHandler)
 
virtual void SetRequestRetryHandler (const RequestRetryHandler &handler)
 
virtual void SetRequestRetryHandler (RequestRetryHandler &&handler)
 
virtual const Aws::Http::DataReceivedEventHandlerGetDataReceivedEventHandler () const
 
virtual const Aws::Http::DataSentEventHandlerGetDataSentEventHandler () const
 
virtual const Aws::Http::ContinueRequestHandlerGetContinueRequestHandler () const
 
virtual const RequestRetryHandlerGetRequestRetryHandler () const
 
virtual bool ShouldComputeContentMd5 () const
 

Additional Inherited Members

- Protected Member Functions inherited from Aws::AmazonWebServiceRequest
virtual void DumpBodyToUrl (Aws::Http::URI &uri) const
 

Detailed Description

Definition at line 23 of file CreateMLModelRequest.h.

Constructor & Destructor Documentation

◆ CreateMLModelRequest()

Aws::MachineLearning::Model::CreateMLModelRequest::CreateMLModelRequest ( )

Member Function Documentation

◆ AddParameters() [1/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( Aws::String &&  key,
Aws::String &&  value 
)
inline

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.

Definition at line 538 of file CreateMLModelRequest.h.

◆ AddParameters() [2/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( Aws::String &&  key,
const Aws::String value 
)
inline

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.

Definition at line 470 of file CreateMLModelRequest.h.

◆ AddParameters() [3/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( Aws::String &&  key,
const char *  value 
)
inline

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.

Definition at line 606 of file CreateMLModelRequest.h.

◆ AddParameters() [4/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( const Aws::String key,
Aws::String &&  value 
)
inline

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.

Definition at line 504 of file CreateMLModelRequest.h.

◆ AddParameters() [5/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( const Aws::String key,
const Aws::String value 
)
inline

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.

Definition at line 436 of file CreateMLModelRequest.h.

◆ AddParameters() [6/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( const char *  key,
Aws::String &&  value 
)
inline

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.

Definition at line 572 of file CreateMLModelRequest.h.

◆ AddParameters() [7/7]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::AddParameters ( const char *  key,
const char *  value 
)
inline

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.

Definition at line 640 of file CreateMLModelRequest.h.

◆ GetMLModelId()

const Aws::String& Aws::MachineLearning::Model::CreateMLModelRequest::GetMLModelId ( ) const
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 42 of file CreateMLModelRequest.h.

◆ GetMLModelName()

const Aws::String& Aws::MachineLearning::Model::CreateMLModelRequest::GetMLModelName ( ) const
inline

A user-supplied name or description of the MLModel.

Definition at line 83 of file CreateMLModelRequest.h.

◆ GetMLModelType()

const MLModelType& Aws::MachineLearning::Model::CreateMLModelRequest::GetMLModelType ( ) const
inline

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.

Definition at line 132 of file CreateMLModelRequest.h.

◆ GetParameters()

const Aws::Map<Aws::String, Aws::String>& Aws::MachineLearning::Model::CreateMLModelRequest::GetParameters ( ) const
inline

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.

Definition at line 232 of file CreateMLModelRequest.h.

◆ GetRecipe()

const Aws::String& Aws::MachineLearning::Model::CreateMLModelRequest::GetRecipe ( ) const
inline

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.

Definition at line 689 of file CreateMLModelRequest.h.

◆ GetRecipeUri()

const Aws::String& Aws::MachineLearning::Model::CreateMLModelRequest::GetRecipeUri ( ) const
inline

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.

Definition at line 747 of file CreateMLModelRequest.h.

◆ GetRequestSpecificHeaders()

Aws::Http::HeaderValueCollection Aws::MachineLearning::Model::CreateMLModelRequest::GetRequestSpecificHeaders ( ) const
overridevirtual

◆ GetServiceRequestName()

virtual const char* Aws::MachineLearning::Model::CreateMLModelRequest::GetServiceRequestName ( ) const
inlineoverridevirtual

Implements Aws::AmazonWebServiceRequest.

Definition at line 32 of file CreateMLModelRequest.h.

◆ GetTrainingDataSourceId()

const Aws::String& Aws::MachineLearning::Model::CreateMLModelRequest::GetTrainingDataSourceId ( ) const
inline

The DataSource that points to the training data.

Definition at line 646 of file CreateMLModelRequest.h.

◆ MLModelIdHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::MLModelIdHasBeenSet ( ) const
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 47 of file CreateMLModelRequest.h.

◆ MLModelNameHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::MLModelNameHasBeenSet ( ) const
inline

A user-supplied name or description of the MLModel.

Definition at line 88 of file CreateMLModelRequest.h.

◆ MLModelTypeHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::MLModelTypeHasBeenSet ( ) const
inline

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.

Definition at line 145 of file CreateMLModelRequest.h.

◆ ParametersHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::ParametersHasBeenSet ( ) const
inline

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.

Definition at line 266 of file CreateMLModelRequest.h.

◆ RecipeHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::RecipeHasBeenSet ( ) const
inline

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.

Definition at line 696 of file CreateMLModelRequest.h.

◆ RecipeUriHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::RecipeUriHasBeenSet ( ) const
inline

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.

Definition at line 755 of file CreateMLModelRequest.h.

◆ SerializePayload()

Aws::String Aws::MachineLearning::Model::CreateMLModelRequest::SerializePayload ( ) const
overridevirtual

Convert payload into String.

Implements Aws::AmazonSerializableWebServiceRequest.

◆ SetMLModelId() [1/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelId ( Aws::String &&  value)
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 57 of file CreateMLModelRequest.h.

◆ SetMLModelId() [2/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelId ( const Aws::String value)
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 52 of file CreateMLModelRequest.h.

◆ SetMLModelId() [3/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelId ( const char *  value)
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 62 of file CreateMLModelRequest.h.

◆ SetMLModelName() [1/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelName ( Aws::String &&  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 98 of file CreateMLModelRequest.h.

◆ SetMLModelName() [2/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelName ( const Aws::String value)
inline

A user-supplied name or description of the MLModel.

Definition at line 93 of file CreateMLModelRequest.h.

◆ SetMLModelName() [3/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelName ( const char *  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 103 of file CreateMLModelRequest.h.

◆ SetMLModelType() [1/2]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelType ( const MLModelType value)
inline

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.

Definition at line 158 of file CreateMLModelRequest.h.

◆ SetMLModelType() [2/2]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetMLModelType ( MLModelType &&  value)
inline

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.

Definition at line 171 of file CreateMLModelRequest.h.

◆ SetParameters() [1/2]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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.

Definition at line 334 of file CreateMLModelRequest.h.

◆ SetParameters() [2/2]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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.

Definition at line 300 of file CreateMLModelRequest.h.

◆ SetRecipe() [1/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetRecipe ( Aws::String &&  value)
inline

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.

Definition at line 710 of file CreateMLModelRequest.h.

◆ SetRecipe() [2/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetRecipe ( const Aws::String value)
inline

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.

Definition at line 703 of file CreateMLModelRequest.h.

◆ SetRecipe() [3/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetRecipe ( const char *  value)
inline

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.

Definition at line 717 of file CreateMLModelRequest.h.

◆ SetRecipeUri() [1/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetRecipeUri ( Aws::String &&  value)
inline

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.

Definition at line 771 of file CreateMLModelRequest.h.

◆ SetRecipeUri() [2/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetRecipeUri ( const Aws::String value)
inline

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.

Definition at line 763 of file CreateMLModelRequest.h.

◆ SetRecipeUri() [3/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetRecipeUri ( const char *  value)
inline

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.

Definition at line 779 of file CreateMLModelRequest.h.

◆ SetTrainingDataSourceId() [1/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetTrainingDataSourceId ( Aws::String &&  value)
inline

The DataSource that points to the training data.

Definition at line 661 of file CreateMLModelRequest.h.

◆ SetTrainingDataSourceId() [2/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetTrainingDataSourceId ( const Aws::String value)
inline

The DataSource that points to the training data.

Definition at line 656 of file CreateMLModelRequest.h.

◆ SetTrainingDataSourceId() [3/3]

void Aws::MachineLearning::Model::CreateMLModelRequest::SetTrainingDataSourceId ( const char *  value)
inline

The DataSource that points to the training data.

Definition at line 666 of file CreateMLModelRequest.h.

◆ TrainingDataSourceIdHasBeenSet()

bool Aws::MachineLearning::Model::CreateMLModelRequest::TrainingDataSourceIdHasBeenSet ( ) const
inline

The DataSource that points to the training data.

Definition at line 651 of file CreateMLModelRequest.h.

◆ WithMLModelId() [1/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelId ( Aws::String &&  value)
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 72 of file CreateMLModelRequest.h.

◆ WithMLModelId() [2/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelId ( const Aws::String value)
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 67 of file CreateMLModelRequest.h.

◆ WithMLModelId() [3/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelId ( const char *  value)
inline

A user-supplied ID that uniquely identifies the MLModel.

Definition at line 77 of file CreateMLModelRequest.h.

◆ WithMLModelName() [1/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelName ( Aws::String &&  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 113 of file CreateMLModelRequest.h.

◆ WithMLModelName() [2/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelName ( const Aws::String value)
inline

A user-supplied name or description of the MLModel.

Definition at line 108 of file CreateMLModelRequest.h.

◆ WithMLModelName() [3/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelName ( const char *  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 118 of file CreateMLModelRequest.h.

◆ WithMLModelType() [1/2]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelType ( const MLModelType value)
inline

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.

Definition at line 184 of file CreateMLModelRequest.h.

◆ WithMLModelType() [2/2]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithMLModelType ( MLModelType &&  value)
inline

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.

Definition at line 197 of file CreateMLModelRequest.h.

◆ WithParameters() [1/2]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithParameters ( Aws::Map< Aws::String, Aws::String > &&  value)
inline

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.

Definition at line 402 of file CreateMLModelRequest.h.

◆ WithParameters() [2/2]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithParameters ( const Aws::Map< Aws::String, Aws::String > &  value)
inline

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.

Definition at line 368 of file CreateMLModelRequest.h.

◆ WithRecipe() [1/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithRecipe ( Aws::String &&  value)
inline

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.

Definition at line 731 of file CreateMLModelRequest.h.

◆ WithRecipe() [2/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithRecipe ( const Aws::String value)
inline

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.

Definition at line 724 of file CreateMLModelRequest.h.

◆ WithRecipe() [3/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithRecipe ( const char *  value)
inline

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.

Definition at line 738 of file CreateMLModelRequest.h.

◆ WithRecipeUri() [1/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithRecipeUri ( Aws::String &&  value)
inline

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.

Definition at line 795 of file CreateMLModelRequest.h.

◆ WithRecipeUri() [2/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithRecipeUri ( const Aws::String value)
inline

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.

Definition at line 787 of file CreateMLModelRequest.h.

◆ WithRecipeUri() [3/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithRecipeUri ( const char *  value)
inline

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.

Definition at line 803 of file CreateMLModelRequest.h.

◆ WithTrainingDataSourceId() [1/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithTrainingDataSourceId ( Aws::String &&  value)
inline

The DataSource that points to the training data.

Definition at line 676 of file CreateMLModelRequest.h.

◆ WithTrainingDataSourceId() [2/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithTrainingDataSourceId ( const Aws::String value)
inline

The DataSource that points to the training data.

Definition at line 671 of file CreateMLModelRequest.h.

◆ WithTrainingDataSourceId() [3/3]

CreateMLModelRequest& Aws::MachineLearning::Model::CreateMLModelRequest::WithTrainingDataSourceId ( const char *  value)
inline

The DataSource that points to the training data.

Definition at line 681 of file CreateMLModelRequest.h.


The documentation for this class was generated from the following file: