Package com.amazonaws.services.machinelearning.model

Class MLModel

java.lang.Object

com.amazonaws.services.machinelearning.model.MLModel

All Implemented Interfaces:

Serializable, Cloneable

public class MLModel
extends Object
implements Serializable, Cloneable

Represents the output of a GetMLModel operation.

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor	Description
MLModel()

Method Summary

Modifier and Type	Method	Description
MLModel	addTrainingParametersEntry(String key, String value)
MLModel	clearTrainingParametersEntries()	Removes all the entries added into TrainingParameters.
MLModel	clone()
boolean	equals(Object obj)
String	getAlgorithm()	The algorithm used to train the MLModel.
Date	getCreatedAt()	The time that the MLModel was created.
String	getCreatedByIamUser()	The AWS user account from which the MLModel was created.
RealtimeEndpointInfo	getEndpointInfo()	The current endpoint of the MLModel.
String	getInputDataLocationS3()	The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
Date	getLastUpdatedAt()	The time of the most recent edit to the MLModel.
String	getMessage()	A description of the most recent details about accessing the MLModel.
String	getMLModelId()	The ID assigned to the MLModel at creation.
String	getMLModelType()	Identifies the MLModel category.
String	getName()	A user-supplied name or description of the MLModel.
Float	getScoreThreshold()
Date	getScoreThresholdLastUpdatedAt()	The time of the most recent edit to the ScoreThreshold.
Long	getSizeInBytes()
String	getStatus()	The current status of an MLModel.
String	getTrainingDataSourceId()	The ID of the training DataSource.
Map<String,String>	getTrainingParameters()	A list of the training parameters in the MLModel.
int	hashCode()
void	setAlgorithm(Algorithm algorithm)	The algorithm used to train the MLModel.
void	setAlgorithm(String algorithm)	The algorithm used to train the MLModel.
void	setCreatedAt(Date createdAt)	The time that the MLModel was created.
void	setCreatedByIamUser(String createdByIamUser)	The AWS user account from which the MLModel was created.
void	setEndpointInfo(RealtimeEndpointInfo endpointInfo)	The current endpoint of the MLModel.
void	setInputDataLocationS3(String inputDataLocationS3)	The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
void	setLastUpdatedAt(Date lastUpdatedAt)	The time of the most recent edit to the MLModel.
void	setMessage(String message)	A description of the most recent details about accessing the MLModel.
void	setMLModelId(String mLModelId)	The ID assigned to the MLModel at creation.
void	setMLModelType(MLModelType mLModelType)	Identifies the MLModel category.
void	setMLModelType(String mLModelType)	Identifies the MLModel category.
void	setName(String name)	A user-supplied name or description of the MLModel.
void	setScoreThreshold(Float scoreThreshold)
void	setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)	The time of the most recent edit to the ScoreThreshold.
void	setSizeInBytes(Long sizeInBytes)
void	setStatus(EntityStatus status)	The current status of an MLModel.
void	setStatus(String status)	The current status of an MLModel.
void	setTrainingDataSourceId(String trainingDataSourceId)	The ID of the training DataSource.
void	setTrainingParameters(Map<String,String> trainingParameters)	A list of the training parameters in the MLModel.
String	toString()	Returns a string representation of this object; useful for testing and debugging.
MLModel	withAlgorithm(Algorithm algorithm)	The algorithm used to train the MLModel.
MLModel	withAlgorithm(String algorithm)	The algorithm used to train the MLModel.
MLModel	withCreatedAt(Date createdAt)	The time that the MLModel was created.
MLModel	withCreatedByIamUser(String createdByIamUser)	The AWS user account from which the MLModel was created.
MLModel	withEndpointInfo(RealtimeEndpointInfo endpointInfo)	The current endpoint of the MLModel.
MLModel	withInputDataLocationS3(String inputDataLocationS3)	The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
MLModel	withLastUpdatedAt(Date lastUpdatedAt)	The time of the most recent edit to the MLModel.
MLModel	withMessage(String message)	A description of the most recent details about accessing the MLModel.
MLModel	withMLModelId(String mLModelId)	The ID assigned to the MLModel at creation.
MLModel	withMLModelType(MLModelType mLModelType)	Identifies the MLModel category.
MLModel	withMLModelType(String mLModelType)	Identifies the MLModel category.
MLModel	withName(String name)	A user-supplied name or description of the MLModel.
MLModel	withScoreThreshold(Float scoreThreshold)
MLModel	withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)	The time of the most recent edit to the ScoreThreshold.
MLModel	withSizeInBytes(Long sizeInBytes)
MLModel	withStatus(EntityStatus status)	The current status of an MLModel.
MLModel	withStatus(String status)	The current status of an MLModel.
MLModel	withTrainingDataSourceId(String trainingDataSourceId)	The ID of the training DataSource.
MLModel	withTrainingParameters(Map<String,String> trainingParameters)	A list of the training parameters in the MLModel.

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

MLModel

public MLModel()

Method Detail

setMLModelId

public void setMLModelId(String mLModelId)

The ID assigned to the MLModel at creation.

Parameters:

mLModelId - The ID assigned to the MLModel at creation.

getMLModelId

public String getMLModelId()

The ID assigned to the MLModel at creation.

Returns:

The ID assigned to the MLModel at creation.

withMLModelId

public MLModel withMLModelId(String mLModelId)

The ID assigned to the MLModel at creation.

Parameters:

mLModelId - The ID assigned to the MLModel at creation.

Returns:

Returns a reference to this object so that method calls can be chained together.

setTrainingDataSourceId

public void setTrainingDataSourceId(String trainingDataSourceId)

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

Parameters:

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

getTrainingDataSourceId

public String getTrainingDataSourceId()

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

Returns:

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

withTrainingDataSourceId

public MLModel withTrainingDataSourceId(String trainingDataSourceId)

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

Parameters:

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

Returns:

Returns a reference to this object so that method calls can be chained together.

setCreatedByIamUser

public void setCreatedByIamUser(String createdByIamUser)

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.

Parameters:

createdByIamUser - 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.

getCreatedByIamUser

public String getCreatedByIamUser()

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.

Returns:

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.

withCreatedByIamUser

public MLModel withCreatedByIamUser(String createdByIamUser)

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.

Parameters:

createdByIamUser - 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

setCreatedAt

public void setCreatedAt(Date createdAt)

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

Parameters:

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

getCreatedAt

public Date getCreatedAt()

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

Returns:

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

withCreatedAt

public MLModel withCreatedAt(Date createdAt)

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

Parameters:

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

Returns:

Returns a reference to this object so that method calls can be chained together.

setLastUpdatedAt

public void setLastUpdatedAt(Date lastUpdatedAt)

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

Parameters:

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

getLastUpdatedAt

public Date getLastUpdatedAt()

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

Returns:

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

withLastUpdatedAt

public MLModel withLastUpdatedAt(Date lastUpdatedAt)

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

Parameters:

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

Returns:

Returns a reference to this object so that method calls can be chained together.

setName

public void setName(String name)

A user-supplied name or description of the MLModel.

Parameters:

name - A user-supplied name or description of the MLModel.

getName

public String getName()

A user-supplied name or description of the MLModel.

Returns:

A user-supplied name or description of the MLModel.

withName

public MLModel withName(String name)

A user-supplied name or description of the MLModel.

Parameters:

name - A user-supplied name or description of the MLModel.

Returns:

Returns a reference to this object so that method calls can be chained together.

setStatus

public void setStatus(String status)

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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Parameters:

status - 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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

See Also:

EntityStatus

getStatus

public String getStatus()

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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Returns:

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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

See Also:

EntityStatus

withStatus

public MLModel withStatus(String status)

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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Parameters:

status - 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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

EntityStatus

setStatus

public void setStatus(EntityStatus status)

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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Parameters:

status - 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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

See Also:

EntityStatus

withStatus

public MLModel withStatus(EntityStatus status)

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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Parameters:

status - 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 did not run to completion. It is not usable.
• COMPLETED - The creation process completed successfully.
• DELETED - The MLModel is marked as deleted. It is not usable.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

EntityStatus

setSizeInBytes

public void setSizeInBytes(Long sizeInBytes)

Parameters:

sizeInBytes -

getSizeInBytes

public Long getSizeInBytes()

Returns:

withSizeInBytes

public MLModel withSizeInBytes(Long sizeInBytes)

Parameters:

sizeInBytes -

Returns:

Returns a reference to this object so that method calls can be chained together.

setEndpointInfo

public void setEndpointInfo(RealtimeEndpointInfo endpointInfo)

The current endpoint of the MLModel.

Parameters:

endpointInfo - The current endpoint of the MLModel.

getEndpointInfo

public RealtimeEndpointInfo getEndpointInfo()

The current endpoint of the MLModel.

Returns:

The current endpoint of the MLModel.

withEndpointInfo

public MLModel withEndpointInfo(RealtimeEndpointInfo endpointInfo)

The current endpoint of the MLModel.

Parameters:

endpointInfo - The current endpoint of the MLModel.

Returns:

Returns a reference to this object so that method calls can be chained together.

getTrainingParameters

public Map<String,String> getTrainingParameters()

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

The following is the current set of training parameters:

• sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Returns:

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

The following is the current set of training parameters:

• sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

setTrainingParameters

public void setTrainingParameters(Map<String,String> trainingParameters)

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

The following is the current set of training parameters:

• sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Parameters:

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

The following is the current set of training parameters:

• sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

withTrainingParameters

public MLModel withTrainingParameters(Map<String,String> trainingParameters)

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

The following is the current set of training parameters:

• sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Parameters:

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

The following is the current set of training parameters:

• sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.

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

• sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

• sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

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

Returns:

Returns a reference to this object so that method calls can be chained together.

addTrainingParametersEntry

public MLModel addTrainingParametersEntry(String key,
                                          String value)

clearTrainingParametersEntries

public MLModel clearTrainingParametersEntries()

Removes all the entries added into TrainingParameters. <p> Returns a reference to this object so that method calls can be chained together.

setInputDataLocationS3

public void setInputDataLocationS3(String inputDataLocationS3)

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

Parameters:

inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

getInputDataLocationS3

public String getInputDataLocationS3()

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

Returns:

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

withInputDataLocationS3

public MLModel withInputDataLocationS3(String inputDataLocationS3)

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

Parameters:

inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

Returns:

Returns a reference to this object so that method calls can be chained together.

setAlgorithm

public void setAlgorithm(String algorithm)

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.

Parameters:

algorithm - 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.

See Also:

Algorithm

getAlgorithm

public String getAlgorithm()

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.

Returns:

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.

See Also:

Algorithm

withAlgorithm

public MLModel withAlgorithm(String algorithm)

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.

Parameters:

algorithm - 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

Algorithm

setAlgorithm

public void setAlgorithm(Algorithm algorithm)

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.

Parameters:

algorithm - 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.

See Also:

Algorithm

withAlgorithm

public MLModel withAlgorithm(Algorithm algorithm)

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.

Parameters:

algorithm - 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

Algorithm

setMLModelType

public void setMLModelType(String mLModelType)

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Parameters:

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

See Also:

MLModelType

getMLModelType

public String getMLModelType()

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Returns:

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

See Also:

MLModelType

withMLModelType

public MLModel withMLModelType(String mLModelType)

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Parameters:

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

MLModelType

setMLModelType

public void setMLModelType(MLModelType mLModelType)

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Parameters:

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

See Also:

MLModelType

withMLModelType

public MLModel withMLModelType(MLModelType mLModelType)

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Parameters:

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

• REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
• BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
• MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

MLModelType

setScoreThreshold

public void setScoreThreshold(Float scoreThreshold)

Parameters:

scoreThreshold -

getScoreThreshold

public Float getScoreThreshold()

Returns:

withScoreThreshold

public MLModel withScoreThreshold(Float scoreThreshold)

Parameters:

scoreThreshold -

Returns:

Returns a reference to this object so that method calls can be chained together.

setScoreThresholdLastUpdatedAt

public void setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)

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

Parameters:

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

getScoreThresholdLastUpdatedAt

public Date getScoreThresholdLastUpdatedAt()

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

Returns:

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

withScoreThresholdLastUpdatedAt

public MLModel withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)

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

Parameters:

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

Returns:

Returns a reference to this object so that method calls can be chained together.

setMessage

public void setMessage(String message)

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

Parameters:

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

getMessage

public String getMessage()

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

Returns:

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

withMessage

public MLModel withMessage(String message)

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

Parameters:

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

Returns:

Returns a reference to this object so that method calls can be chained together.

toString

public String toString()

Returns a string representation of this object; useful for testing and debugging.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public MLModel clone()

Overrides:

clone in class Object