AWS SDK for C++  1.9.124
AWS SDK for C++
Public Member Functions | List of all members
Aws::MachineLearning::Model::MLModel Class Reference

#include <MLModel.h>

Public Member Functions

 MLModel ()
 
 MLModel (Aws::Utils::Json::JsonView jsonValue)
 
MLModeloperator= (Aws::Utils::Json::JsonView jsonValue)
 
Aws::Utils::Json::JsonValue Jsonize () const
 
const Aws::StringGetMLModelId () const
 
bool MLModelIdHasBeenSet () const
 
void SetMLModelId (const Aws::String &value)
 
void SetMLModelId (Aws::String &&value)
 
void SetMLModelId (const char *value)
 
MLModelWithMLModelId (const Aws::String &value)
 
MLModelWithMLModelId (Aws::String &&value)
 
MLModelWithMLModelId (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)
 
MLModelWithTrainingDataSourceId (const Aws::String &value)
 
MLModelWithTrainingDataSourceId (Aws::String &&value)
 
MLModelWithTrainingDataSourceId (const char *value)
 
const Aws::StringGetCreatedByIamUser () const
 
bool CreatedByIamUserHasBeenSet () const
 
void SetCreatedByIamUser (const Aws::String &value)
 
void SetCreatedByIamUser (Aws::String &&value)
 
void SetCreatedByIamUser (const char *value)
 
MLModelWithCreatedByIamUser (const Aws::String &value)
 
MLModelWithCreatedByIamUser (Aws::String &&value)
 
MLModelWithCreatedByIamUser (const char *value)
 
const Aws::Utils::DateTimeGetCreatedAt () const
 
bool CreatedAtHasBeenSet () const
 
void SetCreatedAt (const Aws::Utils::DateTime &value)
 
void SetCreatedAt (Aws::Utils::DateTime &&value)
 
MLModelWithCreatedAt (const Aws::Utils::DateTime &value)
 
MLModelWithCreatedAt (Aws::Utils::DateTime &&value)
 
const Aws::Utils::DateTimeGetLastUpdatedAt () const
 
bool LastUpdatedAtHasBeenSet () const
 
void SetLastUpdatedAt (const Aws::Utils::DateTime &value)
 
void SetLastUpdatedAt (Aws::Utils::DateTime &&value)
 
MLModelWithLastUpdatedAt (const Aws::Utils::DateTime &value)
 
MLModelWithLastUpdatedAt (Aws::Utils::DateTime &&value)
 
const Aws::StringGetName () const
 
bool NameHasBeenSet () const
 
void SetName (const Aws::String &value)
 
void SetName (Aws::String &&value)
 
void SetName (const char *value)
 
MLModelWithName (const Aws::String &value)
 
MLModelWithName (Aws::String &&value)
 
MLModelWithName (const char *value)
 
const EntityStatusGetStatus () const
 
bool StatusHasBeenSet () const
 
void SetStatus (const EntityStatus &value)
 
void SetStatus (EntityStatus &&value)
 
MLModelWithStatus (const EntityStatus &value)
 
MLModelWithStatus (EntityStatus &&value)
 
long long GetSizeInBytes () const
 
bool SizeInBytesHasBeenSet () const
 
void SetSizeInBytes (long long value)
 
MLModelWithSizeInBytes (long long value)
 
const RealtimeEndpointInfoGetEndpointInfo () const
 
bool EndpointInfoHasBeenSet () const
 
void SetEndpointInfo (const RealtimeEndpointInfo &value)
 
void SetEndpointInfo (RealtimeEndpointInfo &&value)
 
MLModelWithEndpointInfo (const RealtimeEndpointInfo &value)
 
MLModelWithEndpointInfo (RealtimeEndpointInfo &&value)
 
const Aws::Map< Aws::String, Aws::String > & GetTrainingParameters () const
 
bool TrainingParametersHasBeenSet () const
 
void SetTrainingParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
void SetTrainingParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
MLModelWithTrainingParameters (const Aws::Map< Aws::String, Aws::String > &value)
 
MLModelWithTrainingParameters (Aws::Map< Aws::String, Aws::String > &&value)
 
MLModelAddTrainingParameters (const Aws::String &key, const Aws::String &value)
 
MLModelAddTrainingParameters (Aws::String &&key, const Aws::String &value)
 
MLModelAddTrainingParameters (const Aws::String &key, Aws::String &&value)
 
MLModelAddTrainingParameters (Aws::String &&key, Aws::String &&value)
 
MLModelAddTrainingParameters (const char *key, Aws::String &&value)
 
MLModelAddTrainingParameters (Aws::String &&key, const char *value)
 
MLModelAddTrainingParameters (const char *key, const char *value)
 
const Aws::StringGetInputDataLocationS3 () const
 
bool InputDataLocationS3HasBeenSet () const
 
void SetInputDataLocationS3 (const Aws::String &value)
 
void SetInputDataLocationS3 (Aws::String &&value)
 
void SetInputDataLocationS3 (const char *value)
 
MLModelWithInputDataLocationS3 (const Aws::String &value)
 
MLModelWithInputDataLocationS3 (Aws::String &&value)
 
MLModelWithInputDataLocationS3 (const char *value)
 
const AlgorithmGetAlgorithm () const
 
bool AlgorithmHasBeenSet () const
 
void SetAlgorithm (const Algorithm &value)
 
void SetAlgorithm (Algorithm &&value)
 
MLModelWithAlgorithm (const Algorithm &value)
 
MLModelWithAlgorithm (Algorithm &&value)
 
const MLModelTypeGetMLModelType () const
 
bool MLModelTypeHasBeenSet () const
 
void SetMLModelType (const MLModelType &value)
 
void SetMLModelType (MLModelType &&value)
 
MLModelWithMLModelType (const MLModelType &value)
 
MLModelWithMLModelType (MLModelType &&value)
 
double GetScoreThreshold () const
 
bool ScoreThresholdHasBeenSet () const
 
void SetScoreThreshold (double value)
 
MLModelWithScoreThreshold (double value)
 
const Aws::Utils::DateTimeGetScoreThresholdLastUpdatedAt () const
 
bool ScoreThresholdLastUpdatedAtHasBeenSet () const
 
void SetScoreThresholdLastUpdatedAt (const Aws::Utils::DateTime &value)
 
void SetScoreThresholdLastUpdatedAt (Aws::Utils::DateTime &&value)
 
MLModelWithScoreThresholdLastUpdatedAt (const Aws::Utils::DateTime &value)
 
MLModelWithScoreThresholdLastUpdatedAt (Aws::Utils::DateTime &&value)
 
const Aws::StringGetMessage () const
 
bool MessageHasBeenSet () const
 
void SetMessage (const Aws::String &value)
 
void SetMessage (Aws::String &&value)
 
void SetMessage (const char *value)
 
MLModelWithMessage (const Aws::String &value)
 
MLModelWithMessage (Aws::String &&value)
 
MLModelWithMessage (const char *value)
 
long long GetComputeTime () const
 
bool ComputeTimeHasBeenSet () const
 
void SetComputeTime (long long value)
 
MLModelWithComputeTime (long long value)
 
const Aws::Utils::DateTimeGetFinishedAt () const
 
bool FinishedAtHasBeenSet () const
 
void SetFinishedAt (const Aws::Utils::DateTime &value)
 
void SetFinishedAt (Aws::Utils::DateTime &&value)
 
MLModelWithFinishedAt (const Aws::Utils::DateTime &value)
 
MLModelWithFinishedAt (Aws::Utils::DateTime &&value)
 
const Aws::Utils::DateTimeGetStartedAt () const
 
bool StartedAtHasBeenSet () const
 
void SetStartedAt (const Aws::Utils::DateTime &value)
 
void SetStartedAt (Aws::Utils::DateTime &&value)
 
MLModelWithStartedAt (const Aws::Utils::DateTime &value)
 
MLModelWithStartedAt (Aws::Utils::DateTime &&value)
 

Detailed Description

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

See Also:

AWS API Reference

Definition at line 39 of file MLModel.h.

Constructor & Destructor Documentation

◆ MLModel() [1/2]

Aws::MachineLearning::Model::MLModel::MLModel ( )

◆ MLModel() [2/2]

Aws::MachineLearning::Model::MLModel::MLModel ( Aws::Utils::Json::JsonView  jsonValue)

Member Function Documentation

◆ AddTrainingParameters() [1/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 769 of file MLModel.h.

◆ AddTrainingParameters() [2/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 703 of file MLModel.h.

◆ AddTrainingParameters() [3/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 835 of file MLModel.h.

◆ AddTrainingParameters() [4/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 736 of file MLModel.h.

◆ AddTrainingParameters() [5/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 670 of file MLModel.h.

◆ AddTrainingParameters() [6/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 802 of file MLModel.h.

◆ AddTrainingParameters() [7/7]

MLModel& Aws::MachineLearning::Model::MLModel::AddTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 868 of file MLModel.h.

◆ AlgorithmHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::AlgorithmHasBeenSet ( ) const
inline

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Definition at line 934 of file MLModel.h.

◆ ComputeTimeHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::ComputeTimeHasBeenSet ( ) const
inline

Definition at line 1139 of file MLModel.h.

◆ CreatedAtHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::CreatedAtHasBeenSet ( ) const
inline

The time that the MLModel was created. The time is expressed in epoch time.

Definition at line 213 of file MLModel.h.

◆ CreatedByIamUserHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::CreatedByIamUserHasBeenSet ( ) const
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 158 of file MLModel.h.

◆ EndpointInfoHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::EndpointInfoHasBeenSet ( ) const
inline

The current endpoint of the MLModel.

Definition at line 418 of file MLModel.h.

◆ FinishedAtHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::FinishedAtHasBeenSet ( ) const
inline

Definition at line 1152 of file MLModel.h.

◆ GetAlgorithm()

const Algorithm& Aws::MachineLearning::Model::MLModel::GetAlgorithm ( ) const
inline

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Definition at line 926 of file MLModel.h.

◆ GetComputeTime()

long long Aws::MachineLearning::Model::MLModel::GetComputeTime ( ) const
inline

Definition at line 1136 of file MLModel.h.

◆ GetCreatedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::MLModel::GetCreatedAt ( ) const
inline

The time that the MLModel was created. The time is expressed in epoch time.

Definition at line 207 of file MLModel.h.

◆ GetCreatedByIamUser()

const Aws::String& Aws::MachineLearning::Model::MLModel::GetCreatedByIamUser ( ) const
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 151 of file MLModel.h.

◆ GetEndpointInfo()

const RealtimeEndpointInfo& Aws::MachineLearning::Model::MLModel::GetEndpointInfo ( ) const
inline

The current endpoint of the MLModel.

Definition at line 413 of file MLModel.h.

◆ GetFinishedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::MLModel::GetFinishedAt ( ) const
inline

Definition at line 1149 of file MLModel.h.

◆ GetInputDataLocationS3()

const Aws::String& Aws::MachineLearning::Model::MLModel::GetInputDataLocationS3 ( ) const
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 875 of file MLModel.h.

◆ GetLastUpdatedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::MLModel::GetLastUpdatedAt ( ) const
inline

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Definition at line 244 of file MLModel.h.

◆ GetMessage()

const Aws::String& Aws::MachineLearning::Model::MLModel::GetMessage ( ) const
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1090 of file MLModel.h.

◆ GetMLModelId()

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

The ID assigned to the MLModel at creation.

Definition at line 51 of file MLModel.h.

◆ GetMLModelType()

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

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Definition at line 978 of file MLModel.h.

◆ GetName()

const Aws::String& Aws::MachineLearning::Model::MLModel::GetName ( ) const
inline

A user-supplied name or description of the MLModel.

Definition at line 280 of file MLModel.h.

◆ GetScoreThreshold()

double Aws::MachineLearning::Model::MLModel::GetScoreThreshold ( ) const
inline

Definition at line 1037 of file MLModel.h.

◆ GetScoreThresholdLastUpdatedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::MLModel::GetScoreThresholdLastUpdatedAt ( ) const
inline

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

Definition at line 1053 of file MLModel.h.

◆ GetSizeInBytes()

long long Aws::MachineLearning::Model::MLModel::GetSizeInBytes ( ) const
inline

Definition at line 398 of file MLModel.h.

◆ GetStartedAt()

const Aws::Utils::DateTime& Aws::MachineLearning::Model::MLModel::GetStartedAt ( ) const
inline

Definition at line 1168 of file MLModel.h.

◆ GetStatus()

const EntityStatus& Aws::MachineLearning::Model::MLModel::GetStatus ( ) const
inline

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

Definition at line 329 of file MLModel.h.

◆ GetTrainingDataSourceId()

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 94 of file MLModel.h.

◆ GetTrainingParameters()

const Aws::Map<Aws::String, Aws::String>& Aws::MachineLearning::Model::MLModel::GetTrainingParameters ( ) 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 472 of file MLModel.h.

◆ InputDataLocationS3HasBeenSet()

bool Aws::MachineLearning::Model::MLModel::InputDataLocationS3HasBeenSet ( ) const
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 881 of file MLModel.h.

◆ Jsonize()

Aws::Utils::Json::JsonValue Aws::MachineLearning::Model::MLModel::Jsonize ( ) const

◆ LastUpdatedAtHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::LastUpdatedAtHasBeenSet ( ) const
inline

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Definition at line 250 of file MLModel.h.

◆ MessageHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::MessageHasBeenSet ( ) const
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1096 of file MLModel.h.

◆ MLModelIdHasBeenSet()

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

The ID assigned to the MLModel at creation.

Definition at line 56 of file MLModel.h.

◆ MLModelTypeHasBeenSet()

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

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Definition at line 989 of file MLModel.h.

◆ NameHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::NameHasBeenSet ( ) const
inline

A user-supplied name or description of the MLModel.

Definition at line 285 of file MLModel.h.

◆ operator=()

MLModel& Aws::MachineLearning::Model::MLModel::operator= ( Aws::Utils::Json::JsonView  jsonValue)

◆ ScoreThresholdHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::ScoreThresholdHasBeenSet ( ) const
inline

Definition at line 1040 of file MLModel.h.

◆ ScoreThresholdLastUpdatedAtHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::ScoreThresholdLastUpdatedAtHasBeenSet ( ) const
inline

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

Definition at line 1059 of file MLModel.h.

◆ SetAlgorithm() [1/2]

void Aws::MachineLearning::Model::MLModel::SetAlgorithm ( Algorithm &&  value)
inline

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Definition at line 950 of file MLModel.h.

◆ SetAlgorithm() [2/2]

void Aws::MachineLearning::Model::MLModel::SetAlgorithm ( const Algorithm value)
inline

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Definition at line 942 of file MLModel.h.

◆ SetComputeTime()

void Aws::MachineLearning::Model::MLModel::SetComputeTime ( long long  value)
inline

Definition at line 1142 of file MLModel.h.

◆ SetCreatedAt() [1/2]

void Aws::MachineLearning::Model::MLModel::SetCreatedAt ( Aws::Utils::DateTime &&  value)
inline

The time that the MLModel was created. The time is expressed in epoch time.

Definition at line 225 of file MLModel.h.

◆ SetCreatedAt() [2/2]

void Aws::MachineLearning::Model::MLModel::SetCreatedAt ( const Aws::Utils::DateTime value)
inline

The time that the MLModel was created. The time is expressed in epoch time.

Definition at line 219 of file MLModel.h.

◆ SetCreatedByIamUser() [1/3]

void Aws::MachineLearning::Model::MLModel::SetCreatedByIamUser ( Aws::String &&  value)
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 172 of file MLModel.h.

◆ SetCreatedByIamUser() [2/3]

void Aws::MachineLearning::Model::MLModel::SetCreatedByIamUser ( const Aws::String value)
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 165 of file MLModel.h.

◆ SetCreatedByIamUser() [3/3]

void Aws::MachineLearning::Model::MLModel::SetCreatedByIamUser ( const char *  value)
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 179 of file MLModel.h.

◆ SetEndpointInfo() [1/2]

void Aws::MachineLearning::Model::MLModel::SetEndpointInfo ( const RealtimeEndpointInfo value)
inline

The current endpoint of the MLModel.

Definition at line 423 of file MLModel.h.

◆ SetEndpointInfo() [2/2]

void Aws::MachineLearning::Model::MLModel::SetEndpointInfo ( RealtimeEndpointInfo &&  value)
inline

The current endpoint of the MLModel.

Definition at line 428 of file MLModel.h.

◆ SetFinishedAt() [1/2]

void Aws::MachineLearning::Model::MLModel::SetFinishedAt ( Aws::Utils::DateTime &&  value)
inline

Definition at line 1158 of file MLModel.h.

◆ SetFinishedAt() [2/2]

void Aws::MachineLearning::Model::MLModel::SetFinishedAt ( const Aws::Utils::DateTime value)
inline

Definition at line 1155 of file MLModel.h.

◆ SetInputDataLocationS3() [1/3]

void Aws::MachineLearning::Model::MLModel::SetInputDataLocationS3 ( Aws::String &&  value)
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 893 of file MLModel.h.

◆ SetInputDataLocationS3() [2/3]

void Aws::MachineLearning::Model::MLModel::SetInputDataLocationS3 ( const Aws::String value)
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 887 of file MLModel.h.

◆ SetInputDataLocationS3() [3/3]

void Aws::MachineLearning::Model::MLModel::SetInputDataLocationS3 ( const char *  value)
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 899 of file MLModel.h.

◆ SetLastUpdatedAt() [1/2]

void Aws::MachineLearning::Model::MLModel::SetLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Definition at line 262 of file MLModel.h.

◆ SetLastUpdatedAt() [2/2]

void Aws::MachineLearning::Model::MLModel::SetLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Definition at line 256 of file MLModel.h.

◆ SetMessage() [1/3]

void Aws::MachineLearning::Model::MLModel::SetMessage ( Aws::String &&  value)
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1108 of file MLModel.h.

◆ SetMessage() [2/3]

void Aws::MachineLearning::Model::MLModel::SetMessage ( const Aws::String value)
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1102 of file MLModel.h.

◆ SetMessage() [3/3]

void Aws::MachineLearning::Model::MLModel::SetMessage ( const char *  value)
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1114 of file MLModel.h.

◆ SetMLModelId() [1/3]

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

The ID assigned to the MLModel at creation.

Definition at line 66 of file MLModel.h.

◆ SetMLModelId() [2/3]

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

The ID assigned to the MLModel at creation.

Definition at line 61 of file MLModel.h.

◆ SetMLModelId() [3/3]

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

The ID assigned to the MLModel at creation.

Definition at line 71 of file MLModel.h.

◆ SetMLModelType() [1/2]

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

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Definition at line 1000 of file MLModel.h.

◆ SetMLModelType() [2/2]

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

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Definition at line 1011 of file MLModel.h.

◆ SetName() [1/3]

void Aws::MachineLearning::Model::MLModel::SetName ( Aws::String &&  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 295 of file MLModel.h.

◆ SetName() [2/3]

void Aws::MachineLearning::Model::MLModel::SetName ( const Aws::String value)
inline

A user-supplied name or description of the MLModel.

Definition at line 290 of file MLModel.h.

◆ SetName() [3/3]

void Aws::MachineLearning::Model::MLModel::SetName ( const char *  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 300 of file MLModel.h.

◆ SetScoreThreshold()

void Aws::MachineLearning::Model::MLModel::SetScoreThreshold ( double  value)
inline

Definition at line 1043 of file MLModel.h.

◆ SetScoreThresholdLastUpdatedAt() [1/2]

void Aws::MachineLearning::Model::MLModel::SetScoreThresholdLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

Definition at line 1071 of file MLModel.h.

◆ SetScoreThresholdLastUpdatedAt() [2/2]

void Aws::MachineLearning::Model::MLModel::SetScoreThresholdLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

Definition at line 1065 of file MLModel.h.

◆ SetSizeInBytes()

void Aws::MachineLearning::Model::MLModel::SetSizeInBytes ( long long  value)
inline

Definition at line 404 of file MLModel.h.

◆ SetStartedAt() [1/2]

void Aws::MachineLearning::Model::MLModel::SetStartedAt ( Aws::Utils::DateTime &&  value)
inline

Definition at line 1177 of file MLModel.h.

◆ SetStartedAt() [2/2]

void Aws::MachineLearning::Model::MLModel::SetStartedAt ( const Aws::Utils::DateTime value)
inline

Definition at line 1174 of file MLModel.h.

◆ SetStatus() [1/2]

void Aws::MachineLearning::Model::MLModel::SetStatus ( const EntityStatus value)
inline

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

Definition at line 355 of file MLModel.h.

◆ SetStatus() [2/2]

void Aws::MachineLearning::Model::MLModel::SetStatus ( EntityStatus &&  value)
inline

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

Definition at line 368 of file MLModel.h.

◆ SetTrainingDataSourceId() [1/3]

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 115 of file MLModel.h.

◆ SetTrainingDataSourceId() [2/3]

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 108 of file MLModel.h.

◆ SetTrainingDataSourceId() [3/3]

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 122 of file MLModel.h.

◆ SetTrainingParameters() [1/2]

void Aws::MachineLearning::Model::MLModel::SetTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 571 of file MLModel.h.

◆ SetTrainingParameters() [2/2]

void Aws::MachineLearning::Model::MLModel::SetTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 MLModel.h.

◆ SizeInBytesHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::SizeInBytesHasBeenSet ( ) const
inline

Definition at line 401 of file MLModel.h.

◆ StartedAtHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::StartedAtHasBeenSet ( ) const
inline

Definition at line 1171 of file MLModel.h.

◆ StatusHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::StatusHasBeenSet ( ) const
inline

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

Definition at line 342 of file MLModel.h.

◆ TrainingDataSourceIdHasBeenSet()

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 101 of file MLModel.h.

◆ TrainingParametersHasBeenSet()

bool Aws::MachineLearning::Model::MLModel::TrainingParametersHasBeenSet ( ) 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 505 of file MLModel.h.

◆ WithAlgorithm() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithAlgorithm ( Algorithm &&  value)
inline

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Definition at line 966 of file MLModel.h.

◆ WithAlgorithm() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithAlgorithm ( const Algorithm value)
inline

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

Definition at line 958 of file MLModel.h.

◆ WithComputeTime()

MLModel& Aws::MachineLearning::Model::MLModel::WithComputeTime ( long long  value)
inline

Definition at line 1145 of file MLModel.h.

◆ WithCreatedAt() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithCreatedAt ( Aws::Utils::DateTime &&  value)
inline

The time that the MLModel was created. The time is expressed in epoch time.

Definition at line 237 of file MLModel.h.

◆ WithCreatedAt() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithCreatedAt ( const Aws::Utils::DateTime value)
inline

The time that the MLModel was created. The time is expressed in epoch time.

Definition at line 231 of file MLModel.h.

◆ WithCreatedByIamUser() [1/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithCreatedByIamUser ( Aws::String &&  value)
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 193 of file MLModel.h.

◆ WithCreatedByIamUser() [2/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithCreatedByIamUser ( const Aws::String value)
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 186 of file MLModel.h.

◆ WithCreatedByIamUser() [3/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithCreatedByIamUser ( const char *  value)
inline

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

Definition at line 200 of file MLModel.h.

◆ WithEndpointInfo() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithEndpointInfo ( const RealtimeEndpointInfo value)
inline

The current endpoint of the MLModel.

Definition at line 433 of file MLModel.h.

◆ WithEndpointInfo() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithEndpointInfo ( RealtimeEndpointInfo &&  value)
inline

The current endpoint of the MLModel.

Definition at line 438 of file MLModel.h.

◆ WithFinishedAt() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithFinishedAt ( Aws::Utils::DateTime &&  value)
inline

Definition at line 1164 of file MLModel.h.

◆ WithFinishedAt() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithFinishedAt ( const Aws::Utils::DateTime value)
inline

Definition at line 1161 of file MLModel.h.

◆ WithInputDataLocationS3() [1/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithInputDataLocationS3 ( Aws::String &&  value)
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 911 of file MLModel.h.

◆ WithInputDataLocationS3() [2/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithInputDataLocationS3 ( const Aws::String value)
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 905 of file MLModel.h.

◆ WithInputDataLocationS3() [3/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithInputDataLocationS3 ( const char *  value)
inline

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Definition at line 917 of file MLModel.h.

◆ WithLastUpdatedAt() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Definition at line 274 of file MLModel.h.

◆ WithLastUpdatedAt() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Definition at line 268 of file MLModel.h.

◆ WithMessage() [1/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithMessage ( Aws::String &&  value)
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1126 of file MLModel.h.

◆ WithMessage() [2/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithMessage ( const Aws::String value)
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1120 of file MLModel.h.

◆ WithMessage() [3/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithMessage ( const char *  value)
inline

A description of the most recent details about accessing the MLModel.

Definition at line 1132 of file MLModel.h.

◆ WithMLModelId() [1/3]

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

The ID assigned to the MLModel at creation.

Definition at line 81 of file MLModel.h.

◆ WithMLModelId() [2/3]

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

The ID assigned to the MLModel at creation.

Definition at line 76 of file MLModel.h.

◆ WithMLModelId() [3/3]

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

The ID assigned to the MLModel at creation.

Definition at line 86 of file MLModel.h.

◆ WithMLModelType() [1/2]

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

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Definition at line 1022 of file MLModel.h.

◆ WithMLModelType() [2/2]

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

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Definition at line 1033 of file MLModel.h.

◆ WithName() [1/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithName ( Aws::String &&  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 310 of file MLModel.h.

◆ WithName() [2/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithName ( const Aws::String value)
inline

A user-supplied name or description of the MLModel.

Definition at line 305 of file MLModel.h.

◆ WithName() [3/3]

MLModel& Aws::MachineLearning::Model::MLModel::WithName ( const char *  value)
inline

A user-supplied name or description of the MLModel.

Definition at line 315 of file MLModel.h.

◆ WithScoreThreshold()

MLModel& Aws::MachineLearning::Model::MLModel::WithScoreThreshold ( double  value)
inline

Definition at line 1046 of file MLModel.h.

◆ WithScoreThresholdLastUpdatedAt() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithScoreThresholdLastUpdatedAt ( Aws::Utils::DateTime &&  value)
inline

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

Definition at line 1083 of file MLModel.h.

◆ WithScoreThresholdLastUpdatedAt() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithScoreThresholdLastUpdatedAt ( const Aws::Utils::DateTime value)
inline

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

Definition at line 1077 of file MLModel.h.

◆ WithSizeInBytes()

MLModel& Aws::MachineLearning::Model::MLModel::WithSizeInBytes ( long long  value)
inline

Definition at line 407 of file MLModel.h.

◆ WithStartedAt() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithStartedAt ( Aws::Utils::DateTime &&  value)
inline

Definition at line 1183 of file MLModel.h.

◆ WithStartedAt() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithStartedAt ( const Aws::Utils::DateTime value)
inline

Definition at line 1180 of file MLModel.h.

◆ WithStatus() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithStatus ( const EntityStatus value)
inline

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

Definition at line 381 of file MLModel.h.

◆ WithStatus() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithStatus ( EntityStatus &&  value)
inline

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

Definition at line 394 of file MLModel.h.

◆ WithTrainingDataSourceId() [1/3]

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 136 of file MLModel.h.

◆ WithTrainingDataSourceId() [2/3]

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 129 of file MLModel.h.

◆ WithTrainingDataSourceId() [3/3]

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

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

Definition at line 143 of file MLModel.h.

◆ WithTrainingParameters() [1/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 637 of file MLModel.h.

◆ WithTrainingParameters() [2/2]

MLModel& Aws::MachineLearning::Model::MLModel::WithTrainingParameters ( 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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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 604 of file MLModel.h.


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