Class GetMLModelResult
- All Implemented Interfaces:
Serializable
,Cloneable
Represents the output of a GetMLModel operation, and provides detailed
information about a MLModel
.
- See Also:
-
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionaddTrainingParametersEntry
(String key, String value) Removes all the entries added into TrainingParameters.clone()
boolean
The time that theMLModel
was created.The AWS user account from which theMLModel
was created.The current endpoint of theMLModel
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).The time of the most recent edit to theMLModel
.A link to the file that contains logs of theCreateMLModel
operation.Description of the most recent details about accessing theMLModel
.The MLModel ID which is same as theMLModelId
in the request.Identifies theMLModel
category.getName()
A user-supplied name or description of theMLModel
.The recipe to use when training theMLModel
.The schema used by all of the data files referenced by theDataSource
.The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.The time of the most recent edit to theScoreThreshold
.The current status of theMLModel
.The ID of the trainingDataSource
.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
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
A user-supplied name or description of theMLModel
.void
The recipe to use when training theMLModel
.void
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
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
.toString()
Returns a string representation of this object; useful for testing and debugging.withCreatedAt
(Date createdAt) The time that theMLModel
was created.withCreatedByIamUser
(String createdByIamUser) The AWS user account from which theMLModel
was created.withEndpointInfo
(RealtimeEndpointInfo endpointInfo) The current endpoint of theMLModel
withInputDataLocationS3
(String inputDataLocationS3) The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).withLastUpdatedAt
(Date lastUpdatedAt) The time of the most recent edit to theMLModel
.withLogUri
(String logUri) A link to the file that contains logs of theCreateMLModel
operation.withMessage
(String message) Description of the most recent details about accessing theMLModel
.withMLModelId
(String mLModelId) The MLModel ID which is same as theMLModelId
in the request.withMLModelType
(MLModelType mLModelType) Identifies theMLModel
category.withMLModelType
(String mLModelType) Identifies theMLModel
category.A user-supplied name or description of theMLModel
.withRecipe
(String recipe) The recipe to use when training theMLModel
.withSchema
(String schema) The schema used by all of the data files referenced by theDataSource
.withScoreThreshold
(Float scoreThreshold) The scoring threshold is used in binary classificationMLModel
s, and marks the boundary between a positive prediction and a negative prediction.withScoreThresholdLastUpdatedAt
(Date scoreThresholdLastUpdatedAt) The time of the most recent edit to theScoreThreshold
.withSizeInBytes
(Long sizeInBytes) withStatus
(EntityStatus status) The current status of theMLModel
.withStatus
(String status) The current status of theMLModel
.withTrainingDataSourceId
(String trainingDataSourceId) The ID of the trainingDataSource
.withTrainingParameters
(Map<String, String> trainingParameters) A list of the training parameters in theMLModel
.
-
Constructor Details
-
GetMLModelResult
public GetMLModelResult()
-
-
Method Details
-
setMLModelId
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
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.
-
withMLModelId
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.
-
setTrainingDataSourceId
The ID of the training
DataSource
.- Parameters:
trainingDataSourceId
- The ID of the trainingDataSource
.
-
getTrainingDataSourceId
The ID of the training
DataSource
.- Returns:
- The ID of the training
DataSource
.
-
withTrainingDataSourceId
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
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.
-
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
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
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.
-
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
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
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.
-
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
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.
-
setName
A user-supplied name or description of the
MLModel
.- Parameters:
name
- A user-supplied name or description of theMLModel
.
-
getName
A user-supplied name or description of the
MLModel
.- Returns:
- A user-supplied name or description of the
MLModel
.
-
withName
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
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:
-
-
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:
-
-
withStatus
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:
-
-
setStatus
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:
-
-
withStatus
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:
-
-
setSizeInBytes
- Parameters:
sizeInBytes
-
-
getSizeInBytes
- Returns:
-
withSizeInBytes
- Parameters:
sizeInBytes
-- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
setEndpointInfo
The current endpoint of the
MLModel
- Parameters:
endpointInfo
- The current endpoint of theMLModel
-
getEndpointInfo
The current endpoint of the
MLModel
- Returns:
- The current endpoint of the
MLModel
-
withEndpointInfo
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
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.
-
-
-
setTrainingParameters
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.
-
-
-
withTrainingParameters
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.
-
-
addTrainingParametersEntry
-
clearTrainingParametersEntries
Removes all the entries added into TrainingParameters. <p> Returns a reference to this object so that method calls can be chained together. -
setInputDataLocationS3
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
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
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
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:
-
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:
-
withMLModelType
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:
-
setMLModelType
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:
-
withMLModelType
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:
-
setScoreThreshold
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
.
-
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
.
-
withScoreThreshold
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.
-
setScoreThresholdLastUpdatedAt
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
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
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.
-
setLogUri
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.
-
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.
-
withLogUri
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.
-
setMessage
Description of the most recent details about accessing the
MLModel
.- Parameters:
message
- Description of the most recent details about accessing theMLModel
.
-
getMessage
Description of the most recent details about accessing the
MLModel
.- Returns:
- Description of the most recent details about accessing the
MLModel
.
-
withMessage
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.
-
setRecipe
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.
-
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.
-
withRecipe
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.
-
setSchema
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.
-
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.
-
withSchema
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
Returns a string representation of this object; useful for testing and debugging. -
equals
-
hashCode
public int hashCode() -
clone
-