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
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Constructor Summary
Constructors Constructor Description MLModel()
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Method Summary
All Methods Instance Methods Concrete Methods 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 theMLModel
.Date
getCreatedAt()
The time that theMLModel
was created.String
getCreatedByIamUser()
The AWS user account from which theMLModel
was created.RealtimeEndpointInfo
getEndpointInfo()
The current endpoint of theMLModel
.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 theMLModel
.String
getMessage()
A description of the most recent details about accessing theMLModel
.String
getMLModelId()
The ID assigned to theMLModel
at creation.String
getMLModelType()
Identifies theMLModel
category.String
getName()
A user-supplied name or description of theMLModel
.Float
getScoreThreshold()
Date
getScoreThresholdLastUpdatedAt()
The time of the most recent edit to theScoreThreshold
.Long
getSizeInBytes()
String
getStatus()
The current status of anMLModel
.String
getTrainingDataSourceId()
The ID of the trainingDataSource
.Map<String,String>
getTrainingParameters()
A list of the training parameters in theMLModel
.int
hashCode()
void
setAlgorithm(Algorithm algorithm)
The algorithm used to train theMLModel
.void
setAlgorithm(String algorithm)
The algorithm used to train theMLModel
.void
setCreatedAt(Date createdAt)
The time that theMLModel
was created.void
setCreatedByIamUser(String createdByIamUser)
The AWS user account from which theMLModel
was created.void
setEndpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of theMLModel
.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 theMLModel
.void
setMessage(String message)
A description of the most recent details about accessing theMLModel
.void
setMLModelId(String mLModelId)
The ID assigned to theMLModel
at creation.void
setMLModelType(MLModelType mLModelType)
Identifies theMLModel
category.void
setMLModelType(String mLModelType)
Identifies theMLModel
category.void
setName(String name)
A user-supplied name or description of theMLModel
.void
setScoreThreshold(Float scoreThreshold)
void
setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
The time of the most recent edit to theScoreThreshold
.void
setSizeInBytes(Long sizeInBytes)
void
setStatus(EntityStatus status)
The current status of anMLModel
.void
setStatus(String status)
The current status of anMLModel
.void
setTrainingDataSourceId(String trainingDataSourceId)
The ID of the trainingDataSource
.void
setTrainingParameters(Map<String,String> trainingParameters)
A list of the training parameters in theMLModel
.String
toString()
Returns a string representation of this object; useful for testing and debugging.MLModel
withAlgorithm(Algorithm algorithm)
The algorithm used to train theMLModel
.MLModel
withAlgorithm(String algorithm)
The algorithm used to train theMLModel
.MLModel
withCreatedAt(Date createdAt)
The time that theMLModel
was created.MLModel
withCreatedByIamUser(String createdByIamUser)
The AWS user account from which theMLModel
was created.MLModel
withEndpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of theMLModel
.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 theMLModel
.MLModel
withMessage(String message)
A description of the most recent details about accessing theMLModel
.MLModel
withMLModelId(String mLModelId)
The ID assigned to theMLModel
at creation.MLModel
withMLModelType(MLModelType mLModelType)
Identifies theMLModel
category.MLModel
withMLModelType(String mLModelType)
Identifies theMLModel
category.MLModel
withName(String name)
A user-supplied name or description of theMLModel
.MLModel
withScoreThreshold(Float scoreThreshold)
MLModel
withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
The time of the most recent edit to theScoreThreshold
.MLModel
withSizeInBytes(Long sizeInBytes)
MLModel
withStatus(EntityStatus status)
The current status of anMLModel
.MLModel
withStatus(String status)
The current status of anMLModel
.MLModel
withTrainingDataSourceId(String trainingDataSourceId)
The ID of the trainingDataSource
.MLModel
withTrainingParameters(Map<String,String> trainingParameters)
A list of the training parameters in theMLModel
.
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Method Detail
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setMLModelId
public void setMLModelId(String mLModelId)
The ID assigned to the
MLModel
at creation.- Parameters:
mLModelId
- The ID assigned to theMLModel
at creation.
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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 theMLModel
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 theTrainingDataSourceId
.- Parameters:
trainingDataSourceId
- The ID of the trainingDataSource
. The CreateMLModel operation uses theTrainingDataSourceId
.
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getTrainingDataSourceId
public String getTrainingDataSourceId()
The ID of the training
DataSource
. The CreateMLModel operation uses theTrainingDataSourceId
.- Returns:
- The ID of the training
DataSource
. The CreateMLModel operation uses theTrainingDataSourceId
.
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withTrainingDataSourceId
public MLModel withTrainingDataSourceId(String trainingDataSourceId)
The ID of the training
DataSource
. The CreateMLModel operation uses theTrainingDataSourceId
.- Parameters:
trainingDataSourceId
- The ID of the trainingDataSource
. The CreateMLModel operation uses theTrainingDataSourceId
.- 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 theMLModel
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
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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.
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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 theMLModel
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.
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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 theMLModel
was created. The time is expressed in epoch time.
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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.
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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 theMLModel
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 theMLModel
. The time is expressed in epoch time.
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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.
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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 theMLModel
. The time is expressed in epoch time.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setName
public void setName(String name)
A user-supplied name or description of the
MLModel
.- Parameters:
name
- A user-supplied name or description of theMLModel
.
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getName
public String getName()
A user-supplied name or description of the
MLModel
.- Returns:
- A user-supplied name or description of the
MLModel
.
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withName
public MLModel withName(String name)
A user-supplied name or description of the
MLModel
.- Parameters:
name
- A user-supplied name or description of theMLModel
.- 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 anMLModel
. 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.
- PENDING - Amazon Machine Learning (Amazon ML) submitted a
request to create an
- See Also:
EntityStatus
- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
create an
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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.
- PENDING - Amazon Machine Learning (Amazon ML) submitted a
request to create an
- See Also:
EntityStatus
- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
create an
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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 anMLModel
. 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.
- PENDING - Amazon Machine Learning (Amazon ML) submitted a
request to create an
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
EntityStatus
- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
create an
-
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 anMLModel
. 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.
- PENDING - Amazon Machine Learning (Amazon ML) submitted a
request to create an
- See Also:
EntityStatus
- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
create an
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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 anMLModel
. 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.
- PENDING - Amazon Machine Learning (Amazon ML) submitted a
request to create an
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
EntityStatus
- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to
create an
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setSizeInBytes
public void setSizeInBytes(Long sizeInBytes)
- Parameters:
sizeInBytes
-
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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 theMLModel
.
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getEndpointInfo
public RealtimeEndpointInfo getEndpointInfo()
The current endpoint of the
MLModel
.- Returns:
- The current endpoint of the
MLModel
.
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withEndpointInfo
public MLModel withEndpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of the
MLModel
.- Parameters:
endpointInfo
- The current endpoint of theMLModel
.- 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 theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the 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 theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the model size might affect performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
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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 theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the 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 theMLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the model size might affect performance.The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
-
-
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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 theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the 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 theMLModel
. The list is implemented as a map of key/value pairs.The following is the current set of training parameters:
-
sgd.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 theMLModel
. The value is an integer that ranges from 1 to 10000. The default value is 10. -
sgd.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the 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.
-
-
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 theMLModel
. 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 theMLModel
. 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 theMLModel
. 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 theMLModel
. 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 theMLModel
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 theMLModel
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 theMLModel
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 theMLModel
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 theScoreThreshold
. 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 theScoreThreshold
. 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 theMLModel
.
-
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 theMLModel
.- 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 classObject
- Returns:
- A string representation of this object.
- See Also:
Object.toString()
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