Class GetMLModelResult
- java.lang.Object
-
- com.amazonaws.services.machinelearning.model.GetMLModelResult
-
- All Implemented Interfaces:
Serializable
,Cloneable
public class GetMLModelResult extends Object implements Serializable, Cloneable
Represents the output of a GetMLModel operation, and provides detailed information about a
MLModel
.- See Also:
- Serialized Form
-
-
Constructor Summary
Constructors Constructor Description GetMLModelResult()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description GetMLModelResult
addTrainingParametersEntry(String key, String value)
GetMLModelResult
clearTrainingParametersEntries()
Removes all the entries added into TrainingParameters.GetMLModelResult
clone()
boolean
equals(Object obj)
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
getLogUri()
A link to the file that contains logs of theCreateMLModel
operation.String
getMessage()
Description of the most recent details about accessing theMLModel
.String
getMLModelId()
The MLModel ID which is same as theMLModelId
in the request.String
getMLModelType()
Identifies theMLModel
category.String
getName()
A user-supplied name or description of theMLModel
.String
getRecipe()
The recipe to use when training theMLModel
.String
getSchema()
The schema used by all of the data files referenced by theDataSource
.Float
getScoreThreshold()
The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.Date
getScoreThresholdLastUpdatedAt()
The time of the most recent edit to theScoreThreshold
.Long
getSizeInBytes()
String
getStatus()
The current status of theMLModel
.String
getTrainingDataSourceId()
The ID of the trainingDataSource
.Map<String,String>
getTrainingParameters()
A list of the training parameters in theMLModel
.int
hashCode()
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
setLogUri(String logUri)
A link to the file that contains logs of theCreateMLModel
operation.void
setMessage(String message)
Description of the most recent details about accessing theMLModel
.void
setMLModelId(String mLModelId)
The MLModel ID which is same as theMLModelId
in the request.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
setRecipe(String recipe)
The recipe to use when training theMLModel
.void
setSchema(String schema)
The schema used by all of the data files referenced by theDataSource
.void
setScoreThreshold(Float scoreThreshold)
The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.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 theMLModel
.void
setStatus(String status)
The current status of theMLModel
.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.GetMLModelResult
withCreatedAt(Date createdAt)
The time that theMLModel
was created.GetMLModelResult
withCreatedByIamUser(String createdByIamUser)
The AWS user account from which theMLModel
was created.GetMLModelResult
withEndpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of theMLModel
GetMLModelResult
withInputDataLocationS3(String inputDataLocationS3)
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).GetMLModelResult
withLastUpdatedAt(Date lastUpdatedAt)
The time of the most recent edit to theMLModel
.GetMLModelResult
withLogUri(String logUri)
A link to the file that contains logs of theCreateMLModel
operation.GetMLModelResult
withMessage(String message)
Description of the most recent details about accessing theMLModel
.GetMLModelResult
withMLModelId(String mLModelId)
The MLModel ID which is same as theMLModelId
in the request.GetMLModelResult
withMLModelType(MLModelType mLModelType)
Identifies theMLModel
category.GetMLModelResult
withMLModelType(String mLModelType)
Identifies theMLModel
category.GetMLModelResult
withName(String name)
A user-supplied name or description of theMLModel
.GetMLModelResult
withRecipe(String recipe)
The recipe to use when training theMLModel
.GetMLModelResult
withSchema(String schema)
The schema used by all of the data files referenced by theDataSource
.GetMLModelResult
withScoreThreshold(Float scoreThreshold)
The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.GetMLModelResult
withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
The time of the most recent edit to theScoreThreshold
.GetMLModelResult
withSizeInBytes(Long sizeInBytes)
GetMLModelResult
withStatus(EntityStatus status)
The current status of theMLModel
.GetMLModelResult
withStatus(String status)
The current status of theMLModel
.GetMLModelResult
withTrainingDataSourceId(String trainingDataSourceId)
The ID of the trainingDataSource
.GetMLModelResult
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 MLModel ID which is same as the
MLModelId
in the request.- Parameters:
mLModelId
- The MLModel ID which is same as theMLModelId
in the request.
-
getMLModelId
public String getMLModelId()
The MLModel ID which is same as the
MLModelId
in the request.- Returns:
- The MLModel ID which is same as the
MLModelId
in the request.
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withMLModelId
public GetMLModelResult withMLModelId(String mLModelId)
The MLModel ID which is same as the
MLModelId
in the request.- Parameters:
mLModelId
- The MLModel ID which is same as theMLModelId
in the request.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setTrainingDataSourceId
public void setTrainingDataSourceId(String trainingDataSourceId)
The ID of the training
DataSource
.- Parameters:
trainingDataSourceId
- The ID of the trainingDataSource
.
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getTrainingDataSourceId
public String getTrainingDataSourceId()
The ID of the training
DataSource
.- Returns:
- The ID of the training
DataSource
.
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withTrainingDataSourceId
public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId)
The ID of the training
DataSource
.- Parameters:
trainingDataSourceId
- The ID of the trainingDataSource
.- 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 GetMLModelResult 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.
-
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 GetMLModelResult 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 GetMLModelResult 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 GetMLModelResult 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 the
MLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
- Parameters:
status
- The current status of theMLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
-
- See Also:
EntityStatus
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getStatus
public String getStatus()
The current status of the
MLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
- Returns:
- The current status of the
MLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
-
- See Also:
EntityStatus
-
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withStatus
public GetMLModelResult withStatus(String status)
The current status of the
MLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
- Parameters:
status
- The current status of theMLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
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 the
MLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
- Parameters:
status
- The current status of theMLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
-
- See Also:
EntityStatus
-
-
withStatus
public GetMLModelResult withStatus(EntityStatus status)
The current status of the
MLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It is not usable.
- Parameters:
status
- The current status of theMLModel
. This element can have one of the following values:-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to describe aMLModel
. -
INPROGRESS
- The request is processing. -
FAILED
- The request did not run to completion. It is not usable. -
COMPLETED
- The request completed successfully. -
DELETED
- TheMLModel
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
-
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setSizeInBytes
public void setSizeInBytes(Long sizeInBytes)
- Parameters:
sizeInBytes
-
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getSizeInBytes
public Long getSizeInBytes()
- Returns:
-
withSizeInBytes
public GetMLModelResult withSizeInBytes(Long sizeInBytes)
- Parameters:
sizeInBytes
-- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setEndpointInfo
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of the
MLModel
- Parameters:
endpointInfo
- The current endpoint of theMLModel
-
getEndpointInfo
public RealtimeEndpointInfo getEndpointInfo()
The current endpoint of the
MLModel
- Returns:
- The current endpoint of the
MLModel
-
withEndpointInfo
public GetMLModelResult 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 value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- The 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
- The 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 value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- The 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
- The 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 value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- The 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
- The 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 value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- The 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
- The 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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-
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withTrainingParameters
public GetMLModelResult 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 value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- The 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
- The 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 value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when
L1
is specified. Use this parameter sparingly. -
sgd.maxPasses
- The 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
- The 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.
-
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addTrainingParametersEntry
public GetMLModelResult addTrainingParametersEntry(String key, String value)
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clearTrainingParametersEntries
public GetMLModelResult clearTrainingParametersEntries()
Removes all the entries added into TrainingParameters. <p> Returns a reference to this object so that method calls can be chained together.
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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 GetMLModelResult 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.
-
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 an e-commerce website?"
- 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 an e-commerce website?"
- 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 an e-commerce website?"
- 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 an e-commerce website?"
- MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
- See Also:
MLModelType
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withMLModelType
public GetMLModelResult 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 an e-commerce website?"
- 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 an e-commerce website?"
- 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
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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 an e-commerce website?"
- 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 an e-commerce website?"
- MULTICLASS -- Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"
- See Also:
MLModelType
-
withMLModelType
public GetMLModelResult 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 an e-commerce website?"
- 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 an e-commerce website?"
- 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
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setScoreThreshold
public void setScoreThreshold(Float scoreThreshold)
The scoring threshold is used in binary classification
MLModel
s, and marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
true
. Output values less than the threshold receive a negative response from the MLModel, such asfalse
.- Parameters:
scoreThreshold
- The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
true
. Output values less than the threshold receive a negative response from the MLModel, such asfalse
.
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getScoreThreshold
public Float getScoreThreshold()
The scoring threshold is used in binary classification
MLModel
s, and marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
true
. Output values less than the threshold receive a negative response from the MLModel, such asfalse
.- Returns:
- The scoring threshold is used in binary classification
MLModel
s, and marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
true
. Output values less than the threshold receive a negative response from the MLModel, such asfalse
.
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withScoreThreshold
public GetMLModelResult withScoreThreshold(Float scoreThreshold)
The scoring threshold is used in binary classification
MLModel
s, and marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
true
. Output values less than the threshold receive a negative response from the MLModel, such asfalse
.- Parameters:
scoreThreshold
- The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as
true
. Output values less than the threshold receive a negative response from the MLModel, such asfalse
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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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.
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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.
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withScoreThresholdLastUpdatedAt
public GetMLModelResult 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.
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setLogUri
public void setLogUri(String logUri)
A link to the file that contains logs of the
CreateMLModel
operation.- Parameters:
logUri
- A link to the file that contains logs of theCreateMLModel
operation.
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getLogUri
public String getLogUri()
A link to the file that contains logs of the
CreateMLModel
operation.- Returns:
- A link to the file that contains logs of the
CreateMLModel
operation.
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withLogUri
public GetMLModelResult withLogUri(String logUri)
A link to the file that contains logs of the
CreateMLModel
operation.- Parameters:
logUri
- A link to the file that contains logs of theCreateMLModel
operation.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setMessage
public void setMessage(String message)
Description of the most recent details about accessing the
MLModel
.- Parameters:
message
- Description of the most recent details about accessing theMLModel
.
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getMessage
public String getMessage()
Description of the most recent details about accessing the
MLModel
.- Returns:
- Description of the most recent details about accessing the
MLModel
.
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withMessage
public GetMLModelResult withMessage(String message)
Description of the most recent details about accessing the
MLModel
.- Parameters:
message
- Description of the most recent details about accessing theMLModel
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setRecipe
public void setRecipe(String recipe)
The recipe to use when training the
MLModel
. TheRecipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.Note This parameter is provided as part of the verbose format.
- Parameters:
recipe
- The recipe to use when training theMLModel
. TheRecipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.Note This parameter is provided as part of the verbose format.
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getRecipe
public String getRecipe()
The recipe to use when training the
MLModel
. TheRecipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.Note This parameter is provided as part of the verbose format.
- Returns:
- The recipe to use when training the
MLModel
. TheRecipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.Note This parameter is provided as part of the verbose format.
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withRecipe
public GetMLModelResult withRecipe(String recipe)
The recipe to use when training the
MLModel
. TheRecipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.Note This parameter is provided as part of the verbose format.
- Parameters:
recipe
- The recipe to use when training theMLModel
. TheRecipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.Note This parameter is provided as part of the verbose format.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
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setSchema
public void setSchema(String schema)
The schema used by all of the data files referenced by the
DataSource
.Note This parameter is provided as part of the verbose format.
- Parameters:
schema
- The schema used by all of the data files referenced by theDataSource
.Note This parameter is provided as part of the verbose format.
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getSchema
public String getSchema()
The schema used by all of the data files referenced by the
DataSource
.Note This parameter is provided as part of the verbose format.
- Returns:
- The schema used by all of the data files referenced by the
DataSource
.Note This parameter is provided as part of the verbose format.
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withSchema
public GetMLModelResult withSchema(String schema)
The schema used by all of the data files referenced by the
DataSource
.Note This parameter is provided as part of the verbose format.
- Parameters:
schema
- The schema used by all of the data files referenced by theDataSource
.Note This parameter is provided as part of the verbose format.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
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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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clone
public GetMLModelResult clone()
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