GetMLModelResult (AWS SDK for Java

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 and Description

GetMLModelResult()

Constructors
Constructor and Description
`GetMLModelResult()`

Method Summary

Methods
Modifier and Type	Method and 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 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`	`getLogUri()` A link to the file that contains logs of the `CreateMLModel` operation.
`String`	`getMessage()` Description of the most recent details about accessing the `MLModel`.
`String`	`getMLModelId()` The MLModel ID which is same as the `MLModelId` in the request.
`String`	`getMLModelType()` Identifies the `MLModel` category.
`String`	`getName()` A user-supplied name or description of the `MLModel`.
`String`	`getRecipe()` The recipe to use when training the `MLModel`.
`String`	`getSchema()` The schema used by all of the data files referenced by the `DataSource`.
`Float`	`getScoreThreshold()` The scoring threshold is used in binary classification `MLModel`s, and marks the boundary between a positive prediction and a negative prediction.
`Date`	`getScoreThresholdLastUpdatedAt()` The time of the most recent edit to the `ScoreThreshold`.
`Long`	`getSizeInBytes()`
`String`	`getStatus()` The current status of the `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`	`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`	`setLogUri(String logUri)` A link to the file that contains logs of the `CreateMLModel` operation.
`void`	`setMessage(String message)` Description of the most recent details about accessing the `MLModel`.
`void`	`setMLModelId(String mLModelId)` The MLModel ID which is same as the `MLModelId` in the request.
`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`	`setRecipe(String recipe)` The recipe to use when training the `MLModel`.
`void`	`setSchema(String schema)` The schema used by all of the data files referenced by the `DataSource`.
`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.
`void`	`setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`void`	`setSizeInBytes(Long sizeInBytes)`
`void`	`setStatus(EntityStatus status)` The current status of the `MLModel`.
`void`	`setStatus(String status)` The current status of the `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.
`GetMLModelResult`	`withCreatedAt(Date createdAt)` The time that the `MLModel` was created.
`GetMLModelResult`	`withCreatedByIamUser(String createdByIamUser)` The AWS user account from which the `MLModel` was created.
`GetMLModelResult`	`withEndpointInfo(RealtimeEndpointInfo endpointInfo)` The current endpoint of the `MLModel`
`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 the `MLModel`.
`GetMLModelResult`	`withLogUri(String logUri)` A link to the file that contains logs of the `CreateMLModel` operation.
`GetMLModelResult`	`withMessage(String message)` Description of the most recent details about accessing the `MLModel`.
`GetMLModelResult`	`withMLModelId(String mLModelId)` The MLModel ID which is same as the `MLModelId` in the request.
`GetMLModelResult`	`withMLModelType(MLModelType mLModelType)` Identifies the `MLModel` category.
`GetMLModelResult`	`withMLModelType(String mLModelType)` Identifies the `MLModel` category.
`GetMLModelResult`	`withName(String name)` A user-supplied name or description of the `MLModel`.
`GetMLModelResult`	`withRecipe(String recipe)` The recipe to use when training the `MLModel`.
`GetMLModelResult`	`withSchema(String schema)` The schema used by all of the data files referenced by the `DataSource`.
`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.
`GetMLModelResult`	`withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`GetMLModelResult`	`withSizeInBytes(Long sizeInBytes)`
`GetMLModelResult`	`withStatus(EntityStatus status)` The current status of the `MLModel`.
`GetMLModelResult`	`withStatus(String status)` The current status of the `MLModel`.
`GetMLModelResult`	`withTrainingDataSourceId(String trainingDataSourceId)` The ID of the training `DataSource`.
`GetMLModelResult`	`withTrainingParameters(Map<String,String> trainingParameters)` A list of the training parameters in the `MLModel`.

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

- Constructor Detail
  - GetMLModelResult
```
public GetMLModelResult()
```
- Method Detail
  - 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 the MLModelId 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.
  - 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 the MLModelId in the request.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setTrainingDataSourceId
```
public void setTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource.
    
    Parameters:
    trainingDataSourceId - The ID of the training DataSource.
  - getTrainingDataSourceId
```
public String getTrainingDataSourceId()
```
    The ID of the training DataSource.
    
    Returns:
    The ID of the training DataSource.
  - withTrainingDataSourceId
```
public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource.
    
    Parameters:
    trainingDataSourceId - The ID of the training DataSource.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setCreatedByIamUser
```
public void setCreatedByIamUser(String createdByIamUser)
```
    The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Parameters:
    createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
  - getCreatedByIamUser
```
public String getCreatedByIamUser()
```
    The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Returns:
    The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
  - withCreatedByIamUser
```
public 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 the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setCreatedAt
```
public void setCreatedAt(Date createdAt)
```
    The time that the MLModel was created. The time is expressed in epoch time.
    
    Parameters:
    createdAt - The time that the MLModel was created. The time is expressed in epoch time.
  - getCreatedAt
```
public Date getCreatedAt()
```
    The time that the MLModel was created. The time is expressed in epoch time.
    
    Returns:
    The time that the MLModel was created. The time is expressed in epoch time.
  - withCreatedAt
```
public GetMLModelResult withCreatedAt(Date createdAt)
```
    The time that the MLModel was created. The time is expressed in epoch time.
    
    Parameters:
    createdAt - The time that the MLModel was created. The time is expressed in epoch time.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setLastUpdatedAt
```
public void setLastUpdatedAt(Date lastUpdatedAt)
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Parameters:
    lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
  - getLastUpdatedAt
```
public Date getLastUpdatedAt()
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Returns:
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
  - withLastUpdatedAt
```
public 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 the MLModel. The time is expressed in epoch time.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setName
```
public void setName(String name)
```
    A user-supplied name or description of the MLModel.
    
    Parameters:
    name - A user-supplied name or description of the MLModel.
  - getName
```
public String getName()
```
    A user-supplied name or description of the MLModel.
    
    Returns:
    A user-supplied name or description of the MLModel.
  - withName
```
public GetMLModelResult withName(String name)
```
    A user-supplied name or description of the MLModel.
    
    Parameters:
    name - A user-supplied name or description of the MLModel.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setStatus
```
public void setStatus(String status)
```
    The current status of the MLModel. This element can have one of the following values:
    - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
    - INPROGRESS - The request is processing.
    - FAILED - The request did not run to completion. It is not usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It is not usable.
    Parameters:
    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 a MLModel.
    
    INPROGRESS - The request is processing.
    
    FAILED - The request did not run to completion. It is not usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    See Also:
    EntityStatus
  - 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 a MLModel.
    - INPROGRESS - The request is processing.
    - FAILED - The request did not run to completion. It is not usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel 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 a MLModel.
    
    INPROGRESS - The request is processing.
    
    FAILED - The request did not run to completion. It is not usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    See Also:
    EntityStatus
  - 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 a MLModel.
    - INPROGRESS - The request is processing.
    - FAILED - The request did not run to completion. It is not usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It is not usable.
    Parameters:
    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 a MLModel.
    
    INPROGRESS - The request is processing.
    
    FAILED - The request did not run to completion. It is not usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    Returns:
    Returns a reference to this object so that method calls can be chained together.
    See Also:
    EntityStatus
  - setStatus
```
public void setStatus(EntityStatus status)
```
    The current status of the MLModel. This element can have one of the following values:
    - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
    - INPROGRESS - The request is processing.
    - FAILED - The request did not run to completion. It is not usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It is not usable.
    Parameters:
    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 a MLModel.
    
    INPROGRESS - The request is processing.
    
    FAILED - The request did not run to completion. It is not usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel 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 a MLModel.
    - INPROGRESS - The request is processing.
    - FAILED - The request did not run to completion. It is not usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It is not usable.
    Parameters:
    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 a MLModel.
    
    INPROGRESS - The request is processing.
    
    FAILED - The request did not run to completion. It is not usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It is not usable.
    Returns:
    Returns a reference to this object so that method calls can be chained together.
    See Also:
    EntityStatus
  - setSizeInBytes
```
public void setSizeInBytes(Long sizeInBytes)
```
    Parameters:
    sizeInBytes -
  - getSizeInBytes
```
public Long getSizeInBytes()
```
    Returns:
  - withSizeInBytes
```
public GetMLModelResult withSizeInBytes(Long sizeInBytes)
```
    Parameters:
    sizeInBytes -
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setEndpointInfo
```
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo)
```
    The current endpoint of the MLModel
    
    Parameters:
    endpointInfo - The current endpoint of the MLModel
  - getEndpointInfo
```
public RealtimeEndpointInfo getEndpointInfo()
```
    The current endpoint of the MLModel
    
    Returns:
    The current endpoint of the MLModel
  - withEndpointInfo
```
public GetMLModelResult withEndpointInfo(RealtimeEndpointInfo endpointInfo)
```
    The current endpoint of the MLModel
    
    Parameters:
    endpointInfo - The current endpoint of the MLModel
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - getTrainingParameters
```
public Map<String,String> getTrainingParameters()
```
    A list of the training parameters in the MLModel. The list is implemented as a map of key/value pairs.
    
    The following is the current set of training parameters:
    - sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - Coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, specify a small value, such as 1.0E-04 or 1.0E-08.
      
      The 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 the MLModel. 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 the MLModel. 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
```
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 the MLModel. 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 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 the MLModel. 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
```
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 the MLModel. 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 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 the MLModel. 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
```
public GetMLModelResult addTrainingParametersEntry(String key,
                                          String value)
```
  - 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.
  - 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 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
  - 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
  - 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 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:
    Returns a reference to this object so that method calls can be chained together.
    See Also:
    MLModelType
  - setMLModelType
```
public void setMLModelType(MLModelType mLModelType)
```
    Identifies the MLModel category. The following are the available types:
    - REGRESSION -- Produces a numeric result. For example, "What listing price should a house have?"
    - BINARY -- Produces one of two possible results. For example, "Is this 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 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
  - 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 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:
    Returns a reference to this object so that method calls can be chained together.
    See Also:
    MLModelType
  - setScoreThreshold
```
public void setScoreThreshold(Float scoreThreshold)
```
    The scoring threshold is used in binary classification MLModels, 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 as false.
    
    Parameters:
    scoreThreshold - The scoring threshold is used in binary classification MLModels, 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 as false.
  - getScoreThreshold
```
public Float getScoreThreshold()
```
    The scoring threshold is used in binary classification MLModels, 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 as false.
    
    Returns:
    The scoring threshold is used in binary classification MLModels, 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 as false.
  - withScoreThreshold
```
public GetMLModelResult withScoreThreshold(Float scoreThreshold)
```
    The scoring threshold is used in binary classification MLModels, 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 as false.
    
    Parameters:
    scoreThreshold - The scoring threshold is used in binary classification MLModels, 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 as false.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setScoreThresholdLastUpdatedAt
```
public void setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Parameters:
    scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
  - getScoreThresholdLastUpdatedAt
```
public Date getScoreThresholdLastUpdatedAt()
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Returns:
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
  - withScoreThresholdLastUpdatedAt
```
public 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 the ScoreThreshold. The time is expressed in epoch time.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - 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 the CreateMLModel operation.
  - 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.
  - 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 the CreateMLModel operation.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - 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 the MLModel.
  - 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.
  - 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 the MLModel.
    
    Returns:
    Returns a reference to this object so that method calls can be chained together.
  - setRecipe
```
public void setRecipe(String recipe)
```
    The recipe to use when training the MLModel. The Recipe 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 the MLModel. The Recipe 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
```
public String getRecipe()
```
    The recipe to use when training the MLModel. The Recipe 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. The Recipe 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
```
public GetMLModelResult withRecipe(String recipe)
```
    The recipe to use when training the MLModel. The Recipe 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 the MLModel. The Recipe 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
```
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 the DataSource.
    Note
    This parameter is provided as part of the verbose format.
  - 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.
  - 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 the DataSource.
    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.
  - toString
```
public String toString()
```
    Returns a string representation of this object; useful for testing and debugging.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    A string representation of this object.
    See Also:
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public GetMLModelResult clone()
```
    Overrides:
    
    clone in class Object

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Class GetMLModelResult

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

GetMLModelResult

Method Detail

setMLModelId

getMLModelId

withMLModelId

setTrainingDataSourceId

getTrainingDataSourceId

withTrainingDataSourceId

setCreatedByIamUser

getCreatedByIamUser

withCreatedByIamUser

setCreatedAt

getCreatedAt

withCreatedAt

setLastUpdatedAt

getLastUpdatedAt

withLastUpdatedAt

setName

getName

withName

setStatus

getStatus

withStatus

setStatus

withStatus

setSizeInBytes

getSizeInBytes

withSizeInBytes

setEndpointInfo

getEndpointInfo

withEndpointInfo

getTrainingParameters

setTrainingParameters

withTrainingParameters

addTrainingParametersEntry

clearTrainingParametersEntries

setInputDataLocationS3

getInputDataLocationS3

withInputDataLocationS3

setMLModelType

getMLModelType

withMLModelType

setMLModelType

withMLModelType

setScoreThreshold

getScoreThreshold

withScoreThreshold

setScoreThresholdLastUpdatedAt

getScoreThresholdLastUpdatedAt

withScoreThresholdLastUpdatedAt

setLogUri

getLogUri

withLogUri

setMessage

getMessage

withMessage

setRecipe

getRecipe

withRecipe

setSchema

getSchema

withSchema

toString

equals

hashCode

clone