GetMLModelResult (AWS SDK for Android - Amazon Machine Learning 2.6.17 API)

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

All Implemented Interfaces:

Serializable
```
public class GetMLModelResult
extends Object
implements Serializable
```
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

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`GetMLModelResult`	`addTrainingParametersEntry(String key, String value)` A list of the training parameters in the `MLModel`.
`GetMLModelResult`	`clearTrainingParametersEntries()` Removes all the entries added into TrainingParameters.
`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()` A 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` models.
`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 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)` A 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` models.
`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 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)` A 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` models.
`GetMLModelResult`	`withScoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)` The time of the most recent edit to the `ScoreThreshold`.
`GetMLModelResult`	`withSizeInBytes(Long sizeInBytes)` Long integer type that is a 64-bit signed number.
`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
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - GetMLModelResult
```
public GetMLModelResult()
```
- Method Detail
  - getMLModelId
```
public String getMLModelId()
```
    The MLModel ID, which is same as the MLModelId in the request.
    
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Returns:
    
    The MLModel ID, which is same as the MLModelId in the request.
  - setMLModelId
```
public void setMLModelId(String mLModelId)
```
    The MLModel ID, which is same as the MLModelId in the request.
    
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    
    mLModelId -
    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.
    
    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 MLModel ID, which is same as the MLModelId in the request.
    
    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.
    
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Returns:
    
    The ID of the training DataSource.
  - setTrainingDataSourceId
```
public void setTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource.
    
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    
    trainingDataSourceId -
    The ID of the training DataSource.
  - withTrainingDataSourceId
```
public GetMLModelResult withTrainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource.
    
    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.
    
    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 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.
    
    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 GetMLModelResult 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 GetMLModelResult 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: - 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: - 1024
    
    Parameters:
    
    name -
    A user-supplied name or description of the MLModel.
  - withName
```
public GetMLModelResult 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: - 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 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. The ML model isn't usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    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. The ML model isn't usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't usable.
    See Also:
    
    EntityStatus
  - 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. The ML model isn't usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    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. The ML model isn't usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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. The ML model isn't usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't 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 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. The ML model isn't usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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 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. The ML model isn't usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    Constraints:
    Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
    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. The ML model isn't usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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. The ML model isn't usable.
    - COMPLETED - The request completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't 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 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. The ML model isn't usable.
    
    COMPLETED - The request completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't 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 GetMLModelResult 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 GetMLModelResult 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.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 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. We strongly recommend that you shuffle your data.
    - sgd.l1RegularizationAmount - The 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, 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. It 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 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. We strongly recommend that you shuffle your data.
    
    sgd.l1RegularizationAmount - The 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, 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. It 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 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. We strongly recommend that you shuffle your data.
    - sgd.l1RegularizationAmount - The 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, 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. It 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 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. We strongly recommend that you shuffle your data.
    
    sgd.l1RegularizationAmount - The 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, 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. It 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 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.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 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. We strongly recommend that you shuffle your data.
    - sgd.l1RegularizationAmount - The 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, 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. It 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 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.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 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. We strongly recommend that you shuffle your data.
    
    sgd.l1RegularizationAmount - The 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, 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. It 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 reference to this updated object so that method calls can be chained together.
  - addTrainingParametersEntry
```
public GetMLModelResult 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.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 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. We strongly recommend that you shuffle your data.
    - sgd.l1RegularizationAmount - The 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, 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. It 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.
    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.
    
    Returns:
    
    A reference to this updated object so that method calls can be chained together.
  - clearTrainingParametersEntries
```
public GetMLModelResult 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: - 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: - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    Parameters:
    
    inputDataLocationS3 -
    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).
    
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: - 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.
  - 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 an e-commerce website?"
    - MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    
    BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    
    MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    - BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    - MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    
    BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    
    MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    - BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    - MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    
    BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    
    MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    - BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    - MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    
    BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    
    MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    - BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    - MULTICLASS -- Produces one of several 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 price should a house be listed at?"
    
    BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
    
    MULTICLASS -- Produces one of several 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()
```
    The scoring threshold is used in binary classification MLModel models. It 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 MLModel models. It 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.
  - setScoreThreshold
```
public void setScoreThreshold(Float scoreThreshold)
```
    The scoring threshold is used in binary classification MLModel models. It 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 MLModel models. It 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 MLModel models. It 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 a reference to this object so that method calls can be chained together.
    
    Parameters:
    
    scoreThreshold -
    The scoring threshold is used in binary classification MLModel models. It 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:
    
    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 GetMLModelResult 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.
  - 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.
  - 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.
  - withLogUri
```
public GetMLModelResult withLogUri(String logUri)
```
    A link to the file that contains logs of the CreateMLModel operation.
    
    Returns a reference to this object so that method calls can be chained together.
    
    Parameters:
    
    logUri -
    A link to the file that contains logs of the CreateMLModel operation.
    
    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: - 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: - 10240
    
    Parameters:
    
    message -
    A description of the most recent details about accessing the MLModel.
  - withMessage
```
public GetMLModelResult 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: - 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.
  - 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, and manipulations to perform on the observation data during training.
    Note
    This parameter is provided as part of the verbose format.
    
    Constraints:
    Length: - 131071
    
    Returns:
    
    The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.
    Note
    This parameter is provided as part of the verbose format.
  - 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, and manipulations to perform on the observation data during training.
    Note
    This parameter is provided as part of the verbose format.
    
    Constraints:
    Length: - 131071
    
    Parameters:
    
    recipe -
    The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and 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, and manipulations to perform on the observation data during training.
    Note
    This parameter is provided as part of the verbose format.
    
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: - 131071
    
    Parameters:
    
    recipe -
    The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.
    Note
    This parameter is provided as part of the verbose format.
    
    Returns:
    
    A reference to this updated object so that method calls can be chained together.
  - 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.
    
    Constraints:
    Length: - 131071
    
    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.
  - 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.
    
    Constraints:
    Length: - 131071
    
    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.
  - 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.
    
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: - 131071
    
    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:
    
    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

Class GetMLModelResult

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

GetMLModelResult

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

getMLModelType

setMLModelType

withMLModelType

setMLModelType

withMLModelType

getScoreThreshold

setScoreThreshold

withScoreThreshold

getScoreThresholdLastUpdatedAt

setScoreThresholdLastUpdatedAt

withScoreThresholdLastUpdatedAt

getLogUri

setLogUri

withLogUri

getMessage

setMessage

withMessage

getRecipe

setRecipe

withRecipe

getSchema

setSchema

withSchema

toString

hashCode

equals