MLModel (AWS SDK for Java

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

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class MLModel
extends Object
implements Serializable, Cloneable, StructuredPojo
```
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

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`MLModel`	`addTrainingParametersEntry(String key, String value)`
`MLModel`	`clearTrainingParametersEntries()` Removes all the entries added into TrainingParameters.
`MLModel`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getAlgorithm()` The algorithm used to train the `MLModel`.
`Long`	`getComputeTime()`
`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`.
`Date`	`getFinishedAt()`
`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()`
`Date`	`getScoreThresholdLastUpdatedAt()` The time of the most recent edit to the `ScoreThreshold`.
`Long`	`getSizeInBytes()`
`Date`	`getStartedAt()`
`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`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setAlgorithm(Algorithm algorithm)` The algorithm used to train the `MLModel`.
`void`	`setAlgorithm(String algorithm)` The algorithm used to train the `MLModel`.
`void`	`setComputeTime(Long computeTime)`
`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`	`setFinishedAt(Date finishedAt)`
`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)`
`void`	`setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`void`	`setSizeInBytes(Long sizeInBytes)`
`void`	`setStartedAt(Date startedAt)`
`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`	`withComputeTime(Long computeTime)`
`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`	`withFinishedAt(Date finishedAt)`
`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)`
`MLModel`	`withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`MLModel`	`withSizeInBytes(Long sizeInBytes)`
`MLModel`	`withStartedAt(Date startedAt)`
`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
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - MLModel
```
public MLModel()
```
- Method Detail
  - setMLModelId
```
public void setMLModelId(String mLModelId)
```
    The ID assigned to the MLModel at creation.
    
    Parameters:
    
    mLModelId - The ID assigned to the MLModel at creation.
  - getMLModelId
```
public String getMLModelId()
```
    The ID assigned to the MLModel at creation.
    
    Returns:
    
    The ID assigned to the MLModel at creation.
  - withMLModelId
```
public MLModel withMLModelId(String mLModelId)
```
    The ID assigned to the MLModel at creation.
    
    Parameters:
    
    mLModelId - The ID assigned to the MLModel at creation.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTrainingDataSourceId
```
public void setTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    Parameters:
    
    trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
  - getTrainingDataSourceId
```
public String getTrainingDataSourceId()
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    Returns:
    
    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.
    
    Parameters:
    
    trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    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 MLModel withCreatedByIamUser(String createdByIamUser)
```
    The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Parameters:
    
    createdByIamUser - The AWS user account from which 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 MLModel 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 MLModel withLastUpdatedAt(Date lastUpdatedAt)
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Parameters:
    
    lastUpdatedAt - The time of the most recent edit to 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 MLModel 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 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 didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    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 didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't usable.
    See Also:
    
    EntityStatus
  - 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 didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    Returns:
    The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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 didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    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 didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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 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 didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    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 didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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 didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    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 didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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 MLModel withSizeInBytes(Long sizeInBytes)
```
    Parameters:
    
    sizeInBytes -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setEndpointInfo
```
public void setEndpointInfo(RealtimeEndpointInfo endpointInfo)
```
    The current endpoint of the MLModel.
    
    Parameters:
    
    endpointInfo - The current endpoint of the MLModel.
  - getEndpointInfo
```
public RealtimeEndpointInfo getEndpointInfo()
```
    The current endpoint of the MLModel.
    
    Returns:
    
    The current endpoint of the MLModel.
  - withEndpointInfo
```
public MLModel 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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
      
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    - 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.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
    - sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
    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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
    
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    
    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.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
    
    sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
  - 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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
      
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    - 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.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
    - sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
    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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
    
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    
    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.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
    
    sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
  - 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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
      
      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    - 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.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
    - sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
      
      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
    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.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
    
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    
    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.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
    
    sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - addTrainingParametersEntry
```
public MLModel addTrainingParametersEntry(String key,
                                          String value)
```
  - clearTrainingParametersEntries
```
public MLModel clearTrainingParametersEntries()
```
    Removes all the entries added into TrainingParameters.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setInputDataLocationS3
```
public void setInputDataLocationS3(String inputDataLocationS3)
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Parameters:
    
    inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
  - getInputDataLocationS3
```
public String getInputDataLocationS3()
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Returns:
    
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
  - withInputDataLocationS3
```
public MLModel withInputDataLocationS3(String inputDataLocationS3)
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Parameters:
    
    inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setAlgorithm
```
public void setAlgorithm(String algorithm)
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Parameters:
    algorithm - The algorithm used to train 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
  - getAlgorithm
```
public String getAlgorithm()
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Returns:
    The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    See Also:
    
    Algorithm
  - withAlgorithm
```
public MLModel withAlgorithm(String algorithm)
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Parameters:
    algorithm - The algorithm used to train 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:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Algorithm
  - setAlgorithm
```
public void setAlgorithm(Algorithm algorithm)
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Parameters:
    algorithm - The algorithm used to train 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.
    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:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Algorithm
  - setMLModelType
```
public void setMLModelType(String mLModelType)
```
    Identifies the MLModel category. The following are the available types:
    - REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several 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 price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several 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 price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several 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 price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several 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 price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several 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 price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several 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 price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several 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 price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several 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 price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several 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 price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    MLModelType
  - setScoreThreshold
```
public void setScoreThreshold(Float scoreThreshold)
```
    Parameters:
    
    scoreThreshold -
  - getScoreThreshold
```
public Float getScoreThreshold()
```
    Returns:
  - withScoreThreshold
```
public MLModel withScoreThreshold(Float scoreThreshold)
```
    Parameters:
    
    scoreThreshold -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setScoreThresholdLastUpdatedAt
```
public void setScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Parameters:
    
    scoreThresholdLastUpdatedAt - The time of the most recent edit to 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 MLModel withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Parameters:
    
    scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setMessage
```
public void setMessage(String message)
```
    A description of the most recent details about accessing the MLModel.
    
    Parameters:
    
    message - A description of the most recent details about accessing the MLModel.
  - getMessage
```
public String getMessage()
```
    A description of the most recent details about accessing the MLModel.
    
    Returns:
    
    A description of the most recent details about accessing the MLModel.
  - withMessage
```
public MLModel withMessage(String message)
```
    A description of the most recent details about accessing the MLModel.
    
    Parameters:
    
    message - A description of the most recent details about accessing the MLModel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setComputeTime
```
public void setComputeTime(Long computeTime)
```
    Parameters:
    
    computeTime -
  - getComputeTime
```
public Long getComputeTime()
```
    Returns:
  - withComputeTime
```
public MLModel withComputeTime(Long computeTime)
```
    Parameters:
    
    computeTime -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setFinishedAt
```
public void setFinishedAt(Date finishedAt)
```
    Parameters:
    
    finishedAt -
  - getFinishedAt
```
public Date getFinishedAt()
```
    Returns:
  - withFinishedAt
```
public MLModel withFinishedAt(Date finishedAt)
```
    Parameters:
    
    finishedAt -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setStartedAt
```
public void setStartedAt(Date startedAt)
```
    Parameters:
    
    startedAt -
  - getStartedAt
```
public Date getStartedAt()
```
    Returns:
  - withStartedAt
```
public MLModel withStartedAt(Date startedAt)
```
    Parameters:
    
    startedAt -
    
    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 MLModel clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

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Class MLModel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

MLModel

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

setAlgorithm

getAlgorithm

withAlgorithm

setAlgorithm

withAlgorithm

setMLModelType

getMLModelType

withMLModelType

setMLModelType

withMLModelType

setScoreThreshold

getScoreThreshold

withScoreThreshold

setScoreThresholdLastUpdatedAt

getScoreThresholdLastUpdatedAt

withScoreThresholdLastUpdatedAt

setMessage

getMessage

withMessage

setComputeTime

getComputeTime

withComputeTime

setFinishedAt

getFinishedAt

withFinishedAt

setStartedAt

getStartedAt

withStartedAt

toString

equals

hashCode

clone

marshall