MLModel (AWS SDK For Java for OSGi 1.10.5.1 API)

java.lang.Object
- com.amazonaws.services.machinelearning.model.MLModel

All Implemented Interfaces:

Serializable, Cloneable
```
public class MLModel
extends Object
implements Serializable, Cloneable
```
Represents the output of a GetMLModel operation.

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

See Also:
Serialized Form

Constructor Summary

Constructors
Constructor and Description

MLModel()

Constructors
Constructor and Description
`MLModel()`

Method Summary

Methods
Modifier and Type	Method and Description
`MLModel`	`addTrainingParametersEntry(String key, String value)` A list of the training parameters in the `MLModel`.
`MLModel`	`clearTrainingParametersEntries()` Removes all the entries added into TrainingParameters.
`MLModel`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getAlgorithm()` The algorithm used to train the `MLModel`.
`Date`	`getCreatedAt()` The time that the `MLModel` was created.
`String`	`getCreatedByIamUser()` The AWS user account from which the `MLModel` was created.
`RealtimeEndpointInfo`	`getEndpointInfo()` The current endpoint of the `MLModel`.
`String`	`getInputDataLocationS3()` The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
`Date`	`getLastUpdatedAt()` The time of the most recent edit to the `MLModel`.
`String`	`getMessage()` A description of the most recent details about accessing the `MLModel`.
`String`	`getMLModelId()` The ID assigned to the `MLModel` at creation.
`String`	`getMLModelType()` Identifies the `MLModel` category.
`String`	`getName()` A user-supplied name or description of the `MLModel`.
`Float`	`getScoreThreshold()` Returns the value of the ScoreThreshold property for this object.
`Date`	`getScoreThresholdLastUpdatedAt()` The time of the most recent edit to the `ScoreThreshold`.
`Long`	`getSizeInBytes()` Long integer type that is a 64-bit signed number.
`String`	`getStatus()` The current status of an `MLModel`.
`String`	`getTrainingDataSourceId()` The ID of the training `DataSource`.
`Map<String,String>`	`getTrainingParameters()` A list of the training parameters in the `MLModel`.
`int`	`hashCode()`
`void`	`setAlgorithm(Algorithm algorithm)` The algorithm used to train the `MLModel`.
`void`	`setAlgorithm(String algorithm)` The algorithm used to train the `MLModel`.
`void`	`setCreatedAt(Date createdAt)` The time that the `MLModel` was created.
`void`	`setCreatedByIamUser(String createdByIamUser)` The AWS user account from which the `MLModel` was created.
`void`	`setEndpointInfo(RealtimeEndpointInfo endpointInfo)` The current endpoint of the `MLModel`.
`void`	`setInputDataLocationS3(String inputDataLocationS3)` The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
`void`	`setLastUpdatedAt(Date lastUpdatedAt)` The time of the most recent edit to the `MLModel`.
`void`	`setMessage(String message)` A description of the most recent details about accessing the `MLModel`.
`void`	`setMLModelId(String mLModelId)` The ID assigned to the `MLModel` at creation.
`void`	`setMLModelType(MLModelType mLModelType)` Identifies the `MLModel` category.
`void`	`setMLModelType(String mLModelType)` Identifies the `MLModel` category.
`void`	`setName(String name)` A user-supplied name or description of the `MLModel`.
`void`	`setScoreThreshold(Float scoreThreshold)` Sets the value of the ScoreThreshold property for this object.
`void`	`setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`void`	`setSizeInBytes(Long sizeInBytes)` Long integer type that is a 64-bit signed number.
`void`	`setStatus(EntityStatus status)` The current status of an `MLModel`.
`void`	`setStatus(String status)` The current status of an `MLModel`.
`void`	`setTrainingDataSourceId(String trainingDataSourceId)` The ID of the training `DataSource`.
`void`	`setTrainingParameters(Map<String,String> trainingParameters)` A list of the training parameters in the `MLModel`.
`String`	`toString()` Returns a string representation of this object; useful for testing and debugging.
`MLModel`	`withAlgorithm(Algorithm algorithm)` The algorithm used to train the `MLModel`.
`MLModel`	`withAlgorithm(String algorithm)` The algorithm used to train the `MLModel`.
`MLModel`	`withCreatedAt(Date createdAt)` The time that the `MLModel` was created.
`MLModel`	`withCreatedByIamUser(String createdByIamUser)` The AWS user account from which the `MLModel` was created.
`MLModel`	`withEndpointInfo(RealtimeEndpointInfo endpointInfo)` The current endpoint of the `MLModel`.
`MLModel`	`withInputDataLocationS3(String inputDataLocationS3)` The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
`MLModel`	`withLastUpdatedAt(Date lastUpdatedAt)` The time of the most recent edit to the `MLModel`.
`MLModel`	`withMessage(String message)` A description of the most recent details about accessing the `MLModel`.
`MLModel`	`withMLModelId(String mLModelId)` The ID assigned to the `MLModel` at creation.
`MLModel`	`withMLModelType(MLModelType mLModelType)` Identifies the `MLModel` category.
`MLModel`	`withMLModelType(String mLModelType)` Identifies the `MLModel` category.
`MLModel`	`withName(String name)` A user-supplied name or description of the `MLModel`.
`MLModel`	`withScoreThreshold(Float scoreThreshold)` Sets the value of the ScoreThreshold property for this object.
`MLModel`	`withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`MLModel`	`withSizeInBytes(Long sizeInBytes)` Long integer type that is a 64-bit signed number.
`MLModel`	`withStatus(EntityStatus status)` The current status of an `MLModel`.
`MLModel`	`withStatus(String status)` The current status of an `MLModel`.
`MLModel`	`withTrainingDataSourceId(String trainingDataSourceId)` The ID of the training `DataSource`.
`MLModel`	`withTrainingParameters(Map<String,String> trainingParameters)` A list of the training parameters in the `MLModel`.

Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - MLModel
```
public MLModel()
```
- Method Detail
  - getMLModelId
```
public String getMLModelId()
```
    The ID assigned to the MLModel at creation.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Returns:
    The ID assigned to the MLModel at creation.
  - setMLModelId
```
public void setMLModelId(String mLModelId)
```
    The ID assigned to the MLModel at creation.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    mLModelId - The ID assigned to the MLModel at creation.
  - withMLModelId
```
public MLModel withMLModelId(String mLModelId)
```
    The ID assigned to the MLModel at creation.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    mLModelId - The ID assigned to the MLModel at creation.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getTrainingDataSourceId
```
public String getTrainingDataSourceId()
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Returns:
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
  - setTrainingDataSourceId
```
public void setTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
  - withTrainingDataSourceId
```
public MLModel withTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - 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.
    Constraints:
    Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
    
    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.
  - 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.
    Constraints:
    Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
    
    Parameters:
    createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
  - 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.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
    
    Parameters:
    createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - 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.
  - setCreatedAt
```
public void setCreatedAt(Date createdAt)
```
    The time that the MLModel was created. The time is expressed in epoch time.
    
    Parameters:
    createdAt - The time that the MLModel was created. The time is expressed in epoch time.
  - withCreatedAt
```
public MLModel withCreatedAt(Date createdAt)
```
    The time that the MLModel was created. The time is expressed in epoch time.
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    createdAt - The time that the MLModel was created. The time is expressed in epoch time.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - 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.
  - setLastUpdatedAt
```
public void setLastUpdatedAt(Date lastUpdatedAt)
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Parameters:
    lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
  - withLastUpdatedAt
```
public MLModel withLastUpdatedAt(Date lastUpdatedAt)
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getName
```
public String getName()
```
    A user-supplied name or description of the MLModel.
    Constraints:
    Length: 0 - 1024
    
    Returns:
    A user-supplied name or description of the MLModel.
  - setName
```
public void setName(String name)
```
    A user-supplied name or description of the MLModel.
    Constraints:
    Length: 0 - 1024
    
    Parameters:
    name - A user-supplied name or description of the MLModel.
  - withName
```
public MLModel withName(String name)
```
    A user-supplied name or description of the MLModel.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 0 - 1024
    
    Parameters:
    name - A user-supplied name or description of the MLModel.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - 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.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    Returns:
    The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel did not run to completion. It is not usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    See Also:
    EntityStatus
  - 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.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    Parameters:
    status - The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel did not run to completion. It is not usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    See Also:
    EntityStatus
  - withStatus
```
public MLModel withStatus(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.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    Parameters:
    status - The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel did not run to completion. It is not usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    Returns:
    A reference to this updated object so that method calls can be chained together.
    See Also:
    EntityStatus
  - setStatus
```
public void setStatus(EntityStatus status)
```
    The current status of an MLModel. This element can have one of the following values:
    - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    - INPROGRESS - The creation process is underway.
    - FAILED - The request to create an MLModel did not run to completion. It is not usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It is not usable.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    Parameters:
    status - The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel did not run to completion. It is not usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    See Also:
    EntityStatus
  - withStatus
```
public MLModel withStatus(EntityStatus status)
```
    The current status of an MLModel. This element can have one of the following values:
    - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    - INPROGRESS - The creation process is underway.
    - FAILED - The request to create an MLModel did not run to completion. It is not usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It is not usable.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    Parameters:
    status - The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel did not run to completion. It is not usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    Returns:
    A reference to this updated object so that method calls can be chained together.
    See Also:
    EntityStatus
  - getSizeInBytes
```
public Long getSizeInBytes()
```
    Long integer type that is a 64-bit signed number.
    
    Returns:
    Long integer type that is a 64-bit signed number.
  - setSizeInBytes
```
public void setSizeInBytes(Long sizeInBytes)
```
    Long integer type that is a 64-bit signed number.
    
    Parameters:
    sizeInBytes - Long integer type that is a 64-bit signed number.
  - withSizeInBytes
```
public MLModel withSizeInBytes(Long sizeInBytes)
```
    Long integer type that is a 64-bit signed number.
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    sizeInBytes - Long integer type that is a 64-bit signed number.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getEndpointInfo
```
public RealtimeEndpointInfo getEndpointInfo()
```
    The current endpoint of the MLModel.
    
    Returns:
    The current endpoint of the MLModel.
  - setEndpointInfo
```
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo)
```
    The current endpoint of the MLModel.
    
    Parameters:
    endpointInfo - The current endpoint of the MLModel.
  - withEndpointInfo
```
public MLModel withEndpointInfo(RealtimeEndpointInfo endpointInfo)
```
    The current endpoint of the MLModel.
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    endpointInfo - The current endpoint of the MLModel.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getTrainingParameters
```
public Map<String,String> getTrainingParameters()
```
    A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    - sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    - sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    Returns:
    A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    
    sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    
    sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    
    sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
  - setTrainingParameters
```
public void setTrainingParameters(Map<String,String> trainingParameters)
```
    A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    - sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    - sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    Parameters:
    trainingParameters - A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    
    sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    
    sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    
    sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
  - withTrainingParameters
```
public MLModel withTrainingParameters(Map<String,String> trainingParameters)
```
    A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    - sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    - sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    Returns a reference to this object so that method calls can be chained together.
    Parameters:
    trainingParameters - A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    
    sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    
    sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    
    sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - addTrainingParametersEntry
```
public MLModel addTrainingParametersEntry(String key,
                                 String value)
```
    A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    The following is the current set of training parameters:
    - sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - Number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    - sgd.maxMLModelSizeInBytes - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    The method adds a new key-value pair into TrainingParameters parameter, and returns a reference to this object so that method calls can be chained together.
    Parameters:
    key - The key of the entry to be added into TrainingParameters.
    value - The corresponding value of the entry to be added into TrainingParameters.
  - clearTrainingParametersEntries
```
public MLModel clearTrainingParametersEntries()
```
    Removes all the entries added into TrainingParameters.
    Returns a reference to this object so that method calls can be chained together.
  - getInputDataLocationS3
```
public String getInputDataLocationS3()
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    Constraints:
    Length: 0 - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    Returns:
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
  - setInputDataLocationS3
```
public void setInputDataLocationS3(String inputDataLocationS3)
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    Constraints:
    Length: 0 - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    Parameters:
    inputDataLocationS3 - 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).
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 0 - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    Parameters:
    inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - 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.
    Constraints:
    Allowed Values: sgd
    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
  - 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.
    Constraints:
    Allowed Values: sgd
    Parameters:
    algorithm - The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    See Also:
    Algorithm
  - withAlgorithm
```
public MLModel withAlgorithm(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.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: sgd
    Parameters:
    algorithm - The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Returns:
    A reference to this updated 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.
    Constraints:
    Allowed Values: sgd
    Parameters:
    algorithm - The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    See Also:
    Algorithm
  - withAlgorithm
```
public MLModel withAlgorithm(Algorithm algorithm)
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: sgd
    Parameters:
    algorithm - The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Returns:
    A reference to this updated object so that method calls can be chained together.
    See Also:
    Algorithm
  - 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?".
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    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
  - 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?".
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    mLModelType - Identifies the MLModel category. The following are the available types:
    
    REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
    See Also:
    MLModelType
  - withMLModelType
```
public MLModel withMLModelType(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?".
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    mLModelType - Identifies the MLModel category. The following are the available types:
    
    REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
    Returns:
    A reference to this updated 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?".
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    mLModelType - Identifies the MLModel category. The following are the available types:
    
    REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
    See Also:
    MLModelType
  - withMLModelType
```
public MLModel withMLModelType(MLModelType mLModelType)
```
    Identifies the MLModel category. The following are the available types:
    - REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    mLModelType - Identifies the MLModel category. The following are the available types:
    
    REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".
    Returns:
    A reference to this updated object so that method calls can be chained together.
    See Also:
    MLModelType
  - getScoreThreshold
```
public Float getScoreThreshold()
```
    Returns the value of the ScoreThreshold property for this object.
    
    Returns:
    The value of the ScoreThreshold property for this object.
  - setScoreThreshold
```
public void setScoreThreshold(Float scoreThreshold)
```
    Sets the value of the ScoreThreshold property for this object.
    
    Parameters:
    scoreThreshold - The new value for the ScoreThreshold property for this object.
  - withScoreThreshold
```
public MLModel withScoreThreshold(Float scoreThreshold)
```
    Sets the value of the ScoreThreshold property for this object.
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    scoreThreshold - The new value for the ScoreThreshold property for this object.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - 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.
  - setScoreThresholdLastUpdatedAt
```
public void setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Parameters:
    scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
  - withScoreThresholdLastUpdatedAt
```
public MLModel withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getMessage
```
public String getMessage()
```
    A description of the most recent details about accessing the MLModel.
    Constraints:
    Length: 0 - 10240
    
    Returns:
    A description of the most recent details about accessing the MLModel.
  - setMessage
```
public void setMessage(String message)
```
    A description of the most recent details about accessing the MLModel.
    Constraints:
    Length: 0 - 10240
    
    Parameters:
    message - 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.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 0 - 10240
    
    Parameters:
    message - A description of the most recent details about accessing the MLModel.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - toString
```
public String toString()
```
    Returns a string representation of this object; useful for testing and debugging.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    A string representation of this object.
    See Also:
    Object.toString()
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - clone
```
public MLModel clone()
```
    Overrides:
    
    clone in class Object

Class MLModel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

MLModel

Method Detail

getMLModelId

setMLModelId

withMLModelId

getTrainingDataSourceId

setTrainingDataSourceId

withTrainingDataSourceId

getCreatedByIamUser

setCreatedByIamUser

withCreatedByIamUser

getCreatedAt

setCreatedAt

withCreatedAt

getLastUpdatedAt

setLastUpdatedAt

withLastUpdatedAt

getName

setName

withName

getStatus

setStatus

withStatus

setStatus

withStatus

getSizeInBytes

setSizeInBytes

withSizeInBytes

getEndpointInfo

setEndpointInfo

withEndpointInfo

getTrainingParameters

setTrainingParameters

withTrainingParameters

addTrainingParametersEntry

clearTrainingParametersEntries

getInputDataLocationS3

setInputDataLocationS3

withInputDataLocationS3

getAlgorithm

setAlgorithm

withAlgorithm

setAlgorithm

withAlgorithm

getMLModelType

setMLModelType

withMLModelType

setMLModelType

withMLModelType

getScoreThreshold

setScoreThreshold

withScoreThreshold

getScoreThresholdLastUpdatedAt

setScoreThresholdLastUpdatedAt

withScoreThresholdLastUpdatedAt

getMessage

setMessage

withMessage

toString

hashCode

equals

clone