# Serialized Form

## Package com.amazonaws.services.machinelearning.model

### Class com.amazonaws.services.machinelearning.model.AddTagsRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### tags

    com.amazonaws.internal.SdkInternalList<T> tags

The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.

#### resourceId

    String resourceId

The ID of the ML object to tag. For example, `exampleModelId`.

#### resourceType

    String resourceType

The type of the ML object to tag.

### Class com.amazonaws.services.machinelearning.model.AddTagsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### resourceId

    String resourceId

The ID of the ML object that was tagged.

#### resourceType

    String resourceType

The type of the ML object that was tagged.

### Class com.amazonaws.services.machinelearning.model.AmazonMachineLearningException extends AmazonServiceException implements Serializable

serialVersionUID:
  1L

### Class com.amazonaws.services.machinelearning.model.BatchPrediction extends Object implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

The ID assigned to the `BatchPrediction` at creation. This value should be identical to the value of the `BatchPredictionID` in the request.

#### mLModelId

    String mLModelId

The ID of the `MLModel` that generated predictions for the `BatchPrediction` request.

#### batchPredictionDataSourceId

    String batchPredictionDataSourceId

The ID of the `DataSource` that points to the group of observations to predict.

#### inputDataLocationS3

    String inputDataLocationS3

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

#### createdByIamUser

    String createdByIamUser

The AWS user account that invoked the `BatchPrediction`. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

#### createdAt

    Date createdAt

The time that the `BatchPrediction` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `BatchPrediction`. The time is expressed in epoch time.

#### name

    String name

A user-supplied name or description of the `BatchPrediction`.

#### status

    String status

The status of the `BatchPrediction`. This element can have one of the following values:

- `PENDING` - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.
- `INPROGRESS` - The process is underway.
- `FAILED` - The request to perform a batch prediction did not run to completion. It is not usable.
- `COMPLETED` - The batch prediction process completed successfully.
- `DELETED` - The `BatchPrediction` is marked as deleted. It is not usable.

#### outputUri

    String outputUri

The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the `s3 key` portion of the `outputURI` field: ':', '//', '/./', '/../'.

#### message

    String message

A description of the most recent details about processing the batch prediction request.

#### computeTime

    Long computeTime

#### finishedAt

    Date finishedAt

#### startedAt

    Date startedAt

#### totalRecordCount

    Long totalRecordCount

#### invalidRecordCount

    Long invalidRecordCount

### Class com.amazonaws.services.machinelearning.model.CreateBatchPredictionRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

A user-supplied ID that uniquely identifies the `BatchPrediction`.

#### batchPredictionName

    String batchPredictionName

A user-supplied name or description of the `BatchPrediction`. `BatchPredictionName` can only use the UTF-8 character set.

#### mLModelId

    String mLModelId

The ID of the `MLModel` that will generate predictions for the group of observations.

#### batchPredictionDataSourceId

    String batchPredictionDataSourceId

The ID of the `DataSource` that points to the group of observations to predict.

#### outputUri

    String outputUri

The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the `s3 key` portion of the `outputURI` field: ':', '//', '/./', '/../'.

Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the Amazon Machine Learning Developer Guide.

### Class com.amazonaws.services.machinelearning.model.CreateBatchPredictionResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

A user-supplied ID that uniquely identifies the `BatchPrediction`. This value is identical to the value of the `BatchPredictionId` in the request.

### Class com.amazonaws.services.machinelearning.model.CreateDataSourceFromRDSRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the `DataSource`. Typically, an Amazon Resource Number (ARN) becomes the ID for a `DataSource`.

#### dataSourceName

    String dataSourceName

A user-supplied name or description of the `DataSource`.

#### rDSData

    RDSDataSpec rDSData

The data specification of an Amazon RDS `DataSource`:

- DatabaseInformation -

  - `DatabaseName` - The name of the Amazon RDS database.
  - `InstanceIdentifier` - A unique identifier for the Amazon RDS database instance.

- DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.

- ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see Role templates for data pipelines.

- ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

- SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [ `SubnetId`, `SecurityGroupIds`] pair for a VPC-based RDS DB instance.

- SelectSqlQuery - A query that is used to retrieve the observation data for the `Datasource`.

- S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using `SelectSqlQuery` is stored in this location.

- DataSchemaUri - The Amazon S3 location of the `DataSchema`.

- DataSchema - A JSON string representing the schema. This is not required if `DataSchemaUri` is specified.

- DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the `Datasource`.

  Sample - `"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`

#### roleARN

    String roleARN

The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the `SelectSqlQuery` query from Amazon RDS to Amazon S3.

#### computeStatistics

    Boolean computeStatistics

The compute statistics for a `DataSource`. The statistics are generated from the observation data referenced by a `DataSource`. Amazon ML uses the statistics internally during `MLModel` training. This parameter must be set to `true` if the ``DataSource`` needs to be used for `MLModel` training.

### Class com.amazonaws.services.machinelearning.model.CreateDataSourceFromRDSResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the `DataSourceID` in the request.

### Class com.amazonaws.services.machinelearning.model.CreateDataSourceFromRedshiftRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the `DataSource`.

#### dataSourceName

    String dataSourceName

A user-supplied name or description of the `DataSource`.

#### dataSpec

    RedshiftDataSpec dataSpec

The data specification of an Amazon Redshift `DataSource`:

- DatabaseInformation -

  - `DatabaseName` - The name of the Amazon Redshift database.
  - `ClusterIdentifier` - The unique ID for the Amazon Redshift cluster.

- DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.

- SelectSqlQuery - The query that is used to retrieve the observation data for the `Datasource`.

- S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the `SelectSqlQuery` query is stored in this location.

- DataSchemaUri - The Amazon S3 location of the `DataSchema`.

- DataSchema - A JSON string representing the schema. This is not required if `DataSchemaUri` is specified.

- DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the `DataSource`.

  Sample - `"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`

#### roleARN

    String roleARN

A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:

- A security group to allow Amazon ML to execute the `SelectSqlQuery` query on an Amazon Redshift cluster

- An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the `S3StagingLocation`

#### computeStatistics

    Boolean computeStatistics

The compute statistics for a `DataSource`. The statistics are generated from the observation data referenced by a `DataSource`. Amazon ML uses the statistics internally during `MLModel` training. This parameter must be set to `true` if the `DataSource` needs to be used for `MLModel` training.

### Class com.amazonaws.services.machinelearning.model.CreateDataSourceFromRedshiftResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the `DataSourceID` in the request.

### Class com.amazonaws.services.machinelearning.model.CreateDataSourceFromS3Request extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied identifier that uniquely identifies the `DataSource`.

#### dataSourceName

    String dataSourceName

A user-supplied name or description of the `DataSource`.

#### dataSpec

    S3DataSpec dataSpec

The data specification of a `DataSource`:

- DataLocationS3 - The Amazon S3 location of the observation data.

- DataSchemaLocationS3 - The Amazon S3 location of the `DataSchema`.

- DataSchema - A JSON string representing the schema. This is not required if `DataSchemaUri` is specified.

- DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the `Datasource`.

  Sample - `"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"`

#### computeStatistics

    Boolean computeStatistics

The compute statistics for a `DataSource`. The statistics are generated from the observation data referenced by a `DataSource`. Amazon ML uses the statistics internally during `MLModel` training. This parameter must be set to `true` if the ``DataSource`` needs to be used for `MLModel` training.

### Class com.amazonaws.services.machinelearning.model.CreateDataSourceFromS3Result extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the `DataSource`. This value should be identical to the value of the `DataSourceID` in the request.

### Class com.amazonaws.services.machinelearning.model.CreateEvaluationRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

A user-supplied ID that uniquely identifies the `Evaluation`.

#### evaluationName

    String evaluationName

A user-supplied name or description of the `Evaluation`.

#### mLModelId

    String mLModelId

The ID of the `MLModel` to evaluate.

The schema used in creating the `MLModel` must match the schema of the `DataSource` used in the `Evaluation`.

#### evaluationDataSourceId

    String evaluationDataSourceId

The ID of the `DataSource` for the evaluation. The schema of the `DataSource` must match the schema used to create the `MLModel`.

### Class com.amazonaws.services.machinelearning.model.CreateEvaluationResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

The user-supplied ID that uniquely identifies the `Evaluation`. This value should be identical to the value of the `EvaluationId` in the request.

### Class com.amazonaws.services.machinelearning.model.CreateMLModelRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A user-supplied ID that uniquely identifies the `MLModel`.

#### mLModelName

    String mLModelName

A user-supplied name or description of the `MLModel`.

#### mLModelType

    String mLModelType

The category of supervised learning that this `MLModel` will address. Choose from the following types:

- Choose `REGRESSION` if the `MLModel` will be used to predict a numeric value.
- Choose `BINARY` if the `MLModel` result has two possible values.
- Choose `MULTICLASS` if the `MLModel` result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

#### parameters

    com.amazonaws.internal.SdkInternalMap<K,V> parameters

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`. 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.

#### trainingDataSourceId

    String trainingDataSourceId

The `DataSource` that points to the training data.

#### recipe

    String recipe

The data recipe for creating the `MLModel`. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

#### recipeUri

    String recipeUri

The Amazon Simple Storage Service (Amazon S3) location and file name that contains the `MLModel` recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

### Class com.amazonaws.services.machinelearning.model.CreateMLModelResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A user-supplied ID that uniquely identifies the `MLModel`. This value should be identical to the value of the `MLModelId` in the request.

### Class com.amazonaws.services.machinelearning.model.CreateRealtimeEndpointRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The ID assigned to the `MLModel` during creation.

### Class com.amazonaws.services.machinelearning.model.CreateRealtimeEndpointResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A user-supplied ID that uniquely identifies the `MLModel`. This value should be identical to the value of the `MLModelId` in the request.

#### realtimeEndpointInfo

    RealtimeEndpointInfo realtimeEndpointInfo

The endpoint information of the `MLModel`

### Class com.amazonaws.services.machinelearning.model.DataSource extends Object implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

The ID that is assigned to the `DataSource` during creation.

#### dataLocationS3

    String dataLocationS3

The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a `DataSource`.

#### dataRearrangement

    String dataRearrangement

A JSON string that represents the splitting and rearrangement requirement used when this `DataSource` was created.

#### createdByIamUser

    String createdByIamUser

The AWS user account from which the `DataSource` was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

#### createdAt

    Date createdAt

The time that the `DataSource` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `BatchPrediction`. The time is expressed in epoch time.

#### dataSizeInBytes

    Long dataSizeInBytes

The total number of observations contained in the data files that the `DataSource` references.

#### numberOfFiles

    Long numberOfFiles

The number of data files referenced by the `DataSource`.

#### name

    String name

A user-supplied name or description of the `DataSource`.

#### status

    String status

The current status of the `DataSource`. This element can have one of the following values:

- PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a `DataSource`.
- INPROGRESS - The creation process is underway.
- FAILED - The request to create a `DataSource` did not run to completion. It is not usable.
- COMPLETED - The creation process completed successfully.
- DELETED - The `DataSource` is marked as deleted. It is not usable.

#### message

    String message

A description of the most recent details about creating the `DataSource`.

#### redshiftMetadata

    RedshiftMetadata redshiftMetadata

#### rDSMetadata

    RDSMetadata rDSMetadata

#### roleARN

    String roleARN

#### computeStatistics

    Boolean computeStatistics

The parameter is `true` if statistics need to be generated from the observation data.

#### computeTime

    Long computeTime

#### finishedAt

    Date finishedAt

#### startedAt

    Date startedAt

### Class com.amazonaws.services.machinelearning.model.DeleteBatchPredictionRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

A user-supplied ID that uniquely identifies the `BatchPrediction`.

### Class com.amazonaws.services.machinelearning.model.DeleteBatchPredictionResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

A user-supplied ID that uniquely identifies the `BatchPrediction`. This value should be identical to the value of the `BatchPredictionID` in the request.

### Class com.amazonaws.services.machinelearning.model.DeleteDataSourceRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the `DataSource`.

### Class com.amazonaws.services.machinelearning.model.DeleteDataSourceResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

A user-supplied ID that uniquely identifies the `DataSource`. This value should be identical to the value of the `DataSourceID` in the request.

### Class com.amazonaws.services.machinelearning.model.DeleteEvaluationRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

A user-supplied ID that uniquely identifies the `Evaluation` to delete.

### Class com.amazonaws.services.machinelearning.model.DeleteEvaluationResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

A user-supplied ID that uniquely identifies the `Evaluation`. This value should be identical to the value of the `EvaluationId` in the request.

### Class com.amazonaws.services.machinelearning.model.DeleteMLModelRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A user-supplied ID that uniquely identifies the `MLModel`.

### Class com.amazonaws.services.machinelearning.model.DeleteMLModelResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A user-supplied ID that uniquely identifies the `MLModel`. This value should be identical to the value of the `MLModelID` in the request.

### Class com.amazonaws.services.machinelearning.model.DeleteRealtimeEndpointRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The ID assigned to the `MLModel` during creation.

### Class com.amazonaws.services.machinelearning.model.DeleteRealtimeEndpointResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A user-supplied ID that uniquely identifies the `MLModel`. This value should be identical to the value of the `MLModelId` in the request.

#### realtimeEndpointInfo

    RealtimeEndpointInfo realtimeEndpointInfo

The endpoint information of the `MLModel`

### Class com.amazonaws.services.machinelearning.model.DeleteTagsRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### tagKeys

    com.amazonaws.internal.SdkInternalList<T> tagKeys

One or more tags to delete.

#### resourceId

    String resourceId

The ID of the tagged ML object. For example, `exampleModelId`.

#### resourceType

    String resourceType

The type of the tagged ML object.

### Class com.amazonaws.services.machinelearning.model.DeleteTagsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### resourceId

    String resourceId

The ID of the ML object from which tags were deleted.

#### resourceType

    String resourceType

The type of the ML object from which tags were deleted.

### Class com.amazonaws.services.machinelearning.model.DescribeBatchPredictionsRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### filterVariable

    String filterVariable

Use one of the following variables to filter a list of `BatchPrediction`:

- `CreatedAt` - Sets the search criteria to the `BatchPrediction` creation date.
- `Status` - Sets the search criteria to the `BatchPrediction` status.
- `Name` - Sets the search criteria to the contents of the `BatchPrediction` `Name`.
- `IAMUser` - Sets the search criteria to the user account that invoked the `BatchPrediction` creation.
- `MLModelId` - Sets the search criteria to the `MLModel` used in the `BatchPrediction`.
- `DataSourceId` - Sets the search criteria to the `DataSource` used in the `BatchPrediction`.
- `DataURI` - Sets the search criteria to the data file(s) used in the `BatchPrediction`. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.

#### eQ

    String eQ

The equal to operator. The `BatchPrediction` results will have `FilterVariable` values that exactly match the value specified with `EQ`.

#### gT

    String gT

The greater than operator. The `BatchPrediction` results will have `FilterVariable` values that are greater than the value specified with `GT`.

#### lT

    String lT

The less than operator. The `BatchPrediction` results will have `FilterVariable` values that are less than the value specified with `LT`.

#### gE

    String gE

The greater than or equal to operator. The `BatchPrediction` results will have `FilterVariable` values that are greater than or equal to the value specified with `GE`.

#### lE

    String lE

The less than or equal to operator. The `BatchPrediction` results will have `FilterVariable` values that are less than or equal to the value specified with `LE`.

#### nE

    String nE

The not equal to operator. The `BatchPrediction` results will have `FilterVariable` values not equal to the value specified with `NE`.

#### prefix

    String prefix

A string that is found at the beginning of a variable, such as `Name` or `Id`.

For example, a `Batch Prediction` operation could have the `Name` `2014-09-09-HolidayGiftMailer`. To search for this `BatchPrediction`, select `Name` for the `FilterVariable` and any of the following strings for the `Prefix`:

- 2014-09

- 2014-09-09

- 2014-09-09-Holiday

#### sortOrder

    String sortOrder

A two-value parameter that determines the sequence of the resulting list of `MLModel`s.

- `asc` - Arranges the list in ascending order (A-Z, 0-9).
- `dsc` - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by `FilterVariable`.

#### nextToken

    String nextToken

An ID of the page in the paginated results.

#### limit

    Integer limit

The number of pages of information to include in the result. The range of acceptable values is `1` through `100`. The default value is `100`.

### Class com.amazonaws.services.machinelearning.model.DescribeBatchPredictionsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### results

    com.amazonaws.internal.SdkInternalList<T> results

A list of `BatchPrediction` objects that meet the search criteria.

#### nextToken

    String nextToken

The ID of the next page in the paginated results that indicates at least one more page follows.

### Class com.amazonaws.services.machinelearning.model.DescribeDataSourcesRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### filterVariable

    String filterVariable

Use one of the following variables to filter a list of `DataSource`:

- `CreatedAt` - Sets the search criteria to `DataSource` creation dates.
- `Status` - Sets the search criteria to `DataSource` statuses.
- `Name` - Sets the search criteria to the contents of `DataSource` `Name`.
- `DataUri` - Sets the search criteria to the URI of data files used to create the `DataSource`. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
- `IAMUser` - Sets the search criteria to the user account that invoked the `DataSource` creation.

#### eQ

    String eQ

The equal to operator. The `DataSource` results will have `FilterVariable` values that exactly match the value specified with `EQ`.

#### gT

    String gT

The greater than operator. The `DataSource` results will have `FilterVariable` values that are greater than the value specified with `GT`.

#### lT

    String lT

The less than operator. The `DataSource` results will have `FilterVariable` values that are less than the value specified with `LT`.

#### gE

    String gE

The greater than or equal to operator. The `DataSource` results will have `FilterVariable` values that are greater than or equal to the value specified with `GE`.

#### lE

    String lE

The less than or equal to operator. The `DataSource` results will have `FilterVariable` values that are less than or equal to the value specified with `LE`.

#### nE

    String nE

The not equal to operator. The `DataSource` results will have `FilterVariable` values not equal to the value specified with `NE`.

#### prefix

    String prefix

A string that is found at the beginning of a variable, such as `Name` or `Id`.

For example, a `DataSource` could have the `Name` `2014-09-09-HolidayGiftMailer` . To search for this `DataSource`, select `Name` for the `FilterVariable` and any of the following strings for the `Prefix`:

- 2014-09

- 2014-09-09

- 2014-09-09-Holiday

#### sortOrder

    String sortOrder

A two-value parameter that determines the sequence of the resulting list of `DataSource`.

- `asc` - Arranges the list in ascending order (A-Z, 0-9).
- `dsc` - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by `FilterVariable`.

#### nextToken

    String nextToken

The ID of the page in the paginated results.

#### limit

    Integer limit

The maximum number of `DataSource` to include in the result.

### Class com.amazonaws.services.machinelearning.model.DescribeDataSourcesResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### results

    com.amazonaws.internal.SdkInternalList<T> results

A list of `DataSource` that meet the search criteria.

#### nextToken

    String nextToken

An ID of the next page in the paginated results that indicates at least one more page follows.

### Class com.amazonaws.services.machinelearning.model.DescribeEvaluationsRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### filterVariable

    String filterVariable

Use one of the following variable to filter a list of `Evaluation` objects:

- `CreatedAt` - Sets the search criteria to the `Evaluation` creation date.
- `Status` - Sets the search criteria to the `Evaluation` status.
- `Name` - Sets the search criteria to the contents of `Evaluation` `Name`.
- `IAMUser` - Sets the search criteria to the user account that invoked an `Evaluation`.
- `MLModelId` - Sets the search criteria to the `MLModel` that was evaluated.
- `DataSourceId` - Sets the search criteria to the `DataSource` used in `Evaluation`.
- `DataUri` - Sets the search criteria to the data file(s) used in `Evaluation`. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.

#### eQ

    String eQ

The equal to operator. The `Evaluation` results will have `FilterVariable` values that exactly match the value specified with `EQ`.

#### gT

    String gT

The greater than operator. The `Evaluation` results will have `FilterVariable` values that are greater than the value specified with `GT`.

#### lT

    String lT

The less than operator. The `Evaluation` results will have `FilterVariable` values that are less than the value specified with `LT`.

#### gE

    String gE

The greater than or equal to operator. The `Evaluation` results will have `FilterVariable` values that are greater than or equal to the value specified with `GE`.

#### lE

    String lE

The less than or equal to operator. The `Evaluation` results will have `FilterVariable` values that are less than or equal to the value specified with `LE`.

#### nE

    String nE

The not equal to operator. The `Evaluation` results will have `FilterVariable` values not equal to the value specified with `NE`.

#### prefix

    String prefix

A string that is found at the beginning of a variable, such as `Name` or `Id`.

For example, an `Evaluation` could have the `Name` `2014-09-09-HolidayGiftMailer`. To search for this `Evaluation`, select `Name` for the `FilterVariable` and any of the following strings for the `Prefix`:

- 2014-09

- 2014-09-09

- 2014-09-09-Holiday

#### sortOrder

    String sortOrder

A two-value parameter that determines the sequence of the resulting list of `Evaluation`.

- `asc` - Arranges the list in ascending order (A-Z, 0-9).
- `dsc` - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by `FilterVariable`.

#### nextToken

    String nextToken

The ID of the page in the paginated results.

#### limit

    Integer limit

The maximum number of `Evaluation` to include in the result.

### Class com.amazonaws.services.machinelearning.model.DescribeEvaluationsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### results

    com.amazonaws.internal.SdkInternalList<T> results

A list of `Evaluation` that meet the search criteria.

#### nextToken

    String nextToken

The ID of the next page in the paginated results that indicates at least one more page follows.

### Class com.amazonaws.services.machinelearning.model.DescribeMLModelsRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### filterVariable

    String filterVariable

Use one of the following variables to filter a list of `MLModel`:

- `CreatedAt` - Sets the search criteria to `MLModel` creation date.
- `Status` - Sets the search criteria to `MLModel` status.
- `Name` - Sets the search criteria to the contents of `MLModel` `Name`.
- `IAMUser` - Sets the search criteria to the user account that invoked the `MLModel` creation.
- `TrainingDataSourceId` - Sets the search criteria to the `DataSource` used to train one or more `MLModel`.
- `RealtimeEndpointStatus` - Sets the search criteria to the `MLModel` real-time endpoint status.
- `MLModelType` - Sets the search criteria to `MLModel` type: binary, regression, or multi-class.
- `Algorithm` - Sets the search criteria to the algorithm that the `MLModel` uses.
- `TrainingDataURI` - Sets the search criteria to the data file(s) used in training a `MLModel`. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

#### eQ

    String eQ

The equal to operator. The `MLModel` results will have `FilterVariable` values that exactly match the value specified with `EQ`.

#### gT

    String gT

The greater than operator. The `MLModel` results will have `FilterVariable` values that are greater than the value specified with `GT`.

#### lT

    String lT

The less than operator. The `MLModel` results will have `FilterVariable` values that are less than the value specified with `LT`.

#### gE

    String gE

The greater than or equal to operator. The `MLModel` results will have `FilterVariable` values that are greater than or equal to the value specified with `GE`.

#### lE

    String lE

The less than or equal to operator. The `MLModel` results will have `FilterVariable` values that are less than or equal to the value specified with `LE`.

#### nE

    String nE

The not equal to operator. The `MLModel` results will have `FilterVariable` values not equal to the value specified with `NE`.

#### prefix

    String prefix

A string that is found at the beginning of a variable, such as `Name` or `Id`.

For example, an `MLModel` could have the `Name` `2014-09-09-HolidayGiftMailer`. To search for this `MLModel`, select `Name` for the `FilterVariable` and any of the following strings for the `Prefix`:

- 2014-09

- 2014-09-09

- 2014-09-09-Holiday

#### sortOrder

    String sortOrder

A two-value parameter that determines the sequence of the resulting list of `MLModel`.

- `asc` - Arranges the list in ascending order (A-Z, 0-9).
- `dsc` - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by `FilterVariable`.

#### nextToken

    String nextToken

The ID of the page in the paginated results.

#### limit

    Integer limit

The number of pages of information to include in the result. The range of acceptable values is `1` through `100`. The default value is `100`.

### Class com.amazonaws.services.machinelearning.model.DescribeMLModelsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### results

    com.amazonaws.internal.SdkInternalList<T> results

A list of `MLModel` that meet the search criteria.

#### nextToken

    String nextToken

The ID of the next page in the paginated results that indicates at least one more page follows.

### Class com.amazonaws.services.machinelearning.model.DescribeTagsRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### resourceId

    String resourceId

The ID of the ML object. For example, `exampleModelId`.

#### resourceType

    String resourceType

The type of the ML object.

### Class com.amazonaws.services.machinelearning.model.DescribeTagsResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### resourceId

    String resourceId

The ID of the tagged ML object.

#### resourceType

    String resourceType

The type of the tagged ML object.

#### tags

    com.amazonaws.internal.SdkInternalList<T> tags

A list of tags associated with the ML object.

### Class com.amazonaws.services.machinelearning.model.Evaluation extends Object implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

The ID that is assigned to the `Evaluation` at creation.

#### mLModelId

    String mLModelId

The ID of the `MLModel` that is the focus of the evaluation.

#### evaluationDataSourceId

    String evaluationDataSourceId

The ID of the `DataSource` that is used to evaluate the `MLModel`.

#### inputDataLocationS3

    String inputDataLocationS3

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

#### createdByIamUser

    String createdByIamUser

The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

#### createdAt

    Date createdAt

The time that the `Evaluation` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `Evaluation`. The time is expressed in epoch time.

#### name

    String name

A user-supplied name or description of the `Evaluation`.

#### status

    String status

The status of the evaluation. This element can have one of the following values:

- `PENDING` - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an `MLModel`.
- `INPROGRESS` - The evaluation is underway.
- `FAILED` - The request to evaluate an `MLModel` did not run to completion. It is not usable.
- `COMPLETED` - The evaluation process completed successfully.
- `DELETED` - The `Evaluation` is marked as deleted. It is not usable.

#### performanceMetrics

    PerformanceMetrics performanceMetrics

Measurements of how well the `MLModel` performed, using observations referenced by the `DataSource`. One of the following metrics is returned, based on the type of the `MLModel`:

- BinaryAUC: A binary `MLModel` uses the Area Under the Curve (AUC) technique to measure performance.

- RegressionRMSE: A regression `MLModel` uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

- MulticlassAvgFScore: A multiclass `MLModel` uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

#### message

    String message

A description of the most recent details about evaluating the `MLModel`.

#### computeTime

    Long computeTime

#### finishedAt

    Date finishedAt

#### startedAt

    Date startedAt

### Class com.amazonaws.services.machinelearning.model.GetBatchPredictionRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

An ID assigned to the `BatchPrediction` at creation.

### Class com.amazonaws.services.machinelearning.model.GetBatchPredictionResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

An ID assigned to the `BatchPrediction` at creation. This value should be identical to the value of the `BatchPredictionID` in the request.

#### mLModelId

    String mLModelId

The ID of the `MLModel` that generated predictions for the `BatchPrediction` request.

#### batchPredictionDataSourceId

    String batchPredictionDataSourceId

The ID of the `DataSource` that was used to create the `BatchPrediction`.

#### inputDataLocationS3

    String inputDataLocationS3

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

#### createdByIamUser

    String createdByIamUser

The AWS user account that invoked the `BatchPrediction`. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

#### createdAt

    Date createdAt

The time when the `BatchPrediction` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to `BatchPrediction`. The time is expressed in epoch time.

#### name

    String name

A user-supplied name or description of the `BatchPrediction`.

#### status

    String status

The status of the `BatchPrediction`, which can be one of the following values:

- `PENDING` - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.
- `INPROGRESS` - The batch predictions are in progress.
- `FAILED` - The request to perform a batch prediction did not run to completion. It is not usable.
- `COMPLETED` - The batch prediction process completed successfully.
- `DELETED` - The `BatchPrediction` is marked as deleted. It is not usable.

#### outputUri

    String outputUri

The location of an Amazon S3 bucket or directory to receive the operation results.

#### logUri

    String logUri

A link to the file that contains logs of the `CreateBatchPrediction` operation.

#### message

    String message

A description of the most recent details about processing the batch prediction request.

#### computeTime

    Long computeTime

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the `BatchPrediction`, normalized and scaled on computation resources. `ComputeTime` is only available if the `BatchPrediction` is in the `COMPLETED` state.

#### finishedAt

    Date finishedAt

The epoch time when Amazon Machine Learning marked the `BatchPrediction` as `COMPLETED` or `FAILED`. `FinishedAt` is only available when the `BatchPrediction` is in the `COMPLETED` or `FAILED` state.

#### startedAt

    Date startedAt

The epoch time when Amazon Machine Learning marked the `BatchPrediction` as `INPROGRESS`. `StartedAt` isn't available if the `BatchPrediction` is in the `PENDING` state.

#### totalRecordCount

    Long totalRecordCount

The number of total records that Amazon Machine Learning saw while processing the `BatchPrediction`.

#### invalidRecordCount

    Long invalidRecordCount

The number of invalid records that Amazon Machine Learning saw while processing the `BatchPrediction`.

### Class com.amazonaws.services.machinelearning.model.GetDataSourceRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

The ID assigned to the `DataSource` at creation.

#### verbose

    Boolean verbose

Specifies whether the `GetDataSource` operation should return `DataSourceSchema`.

If true, `DataSourceSchema` is returned.

If false, `DataSourceSchema` is not returned.

### Class com.amazonaws.services.machinelearning.model.GetDataSourceResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

The ID assigned to the `DataSource` at creation. This value should be identical to the value of the `DataSourceId` in the request.

#### dataLocationS3

    String dataLocationS3

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

#### dataRearrangement

    String dataRearrangement

A JSON string that represents the splitting and rearrangement requirement used when this `DataSource` was created.

#### createdByIamUser

    String createdByIamUser

The AWS user account from which the `DataSource` was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

#### createdAt

    Date createdAt

The time that the `DataSource` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `DataSource`. The time is expressed in epoch time.

#### dataSizeInBytes

    Long dataSizeInBytes

The total size of observations in the data files.

#### numberOfFiles

    Long numberOfFiles

The number of data files referenced by the `DataSource`.

#### name

    String name

A user-supplied name or description of the `DataSource`.

#### status

    String status

The current status of the `DataSource`. This element can have one of the following values:

- `PENDING` - Amazon ML submitted a request to create a `DataSource`.
- `INPROGRESS` - The creation process is underway.
- `FAILED` - The request to create a `DataSource` did not run to completion. It is not usable.
- `COMPLETED` - The creation process completed successfully.
- `DELETED` - The `DataSource` is marked as deleted. It is not usable.

#### logUri

    String logUri

A link to the file containing logs of `CreateDataSourceFrom*` operations.

#### message

    String message

The user-supplied description of the most recent details about creating the `DataSource`.

#### redshiftMetadata

    RedshiftMetadata redshiftMetadata

#### rDSMetadata

    RDSMetadata rDSMetadata

#### roleARN

    String roleARN

#### computeStatistics

    Boolean computeStatistics

The parameter is `true` if statistics need to be generated from the observation data.

#### computeTime

    Long computeTime

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the `DataSource`, normalized and scaled on computation resources. `ComputeTime` is only available if the `DataSource` is in the `COMPLETED` state and the `ComputeStatistics` is set to true.

#### finishedAt

    Date finishedAt

The epoch time when Amazon Machine Learning marked the `DataSource` as `COMPLETED` or `FAILED`. `FinishedAt` is only available when the `DataSource` is in the `COMPLETED` or `FAILED` state.

#### startedAt

    Date startedAt

The epoch time when Amazon Machine Learning marked the `DataSource` as `INPROGRESS`. `StartedAt` isn't available if the `DataSource` is in the `PENDING` state.

#### dataSourceSchema

    String dataSourceSchema

The schema used by all of the data files of this `DataSource`.

<note>Note

This parameter is provided as part of the verbose format.

</note>

### Class com.amazonaws.services.machinelearning.model.GetEvaluationRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

The ID of the `Evaluation` to retrieve. The evaluation of each `MLModel` is recorded and cataloged. The ID provides the means to access the information.

### Class com.amazonaws.services.machinelearning.model.GetEvaluationResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

The evaluation ID which is same as the `EvaluationId` in the request.

#### mLModelId

    String mLModelId

The ID of the `MLModel` that was the focus of the evaluation.

#### evaluationDataSourceId

    String evaluationDataSourceId

The `DataSource` used for this evaluation.

#### inputDataLocationS3

    String inputDataLocationS3

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

#### createdByIamUser

    String createdByIamUser

The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

#### createdAt

    Date createdAt

The time that the `Evaluation` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `Evaluation`. The time is expressed in epoch time.

#### name

    String name

A user-supplied name or description of the `Evaluation`.

#### status

    String status

The status of the evaluation. This element can have one of the following values:

- `PENDING` - Amazon Machine Language (Amazon ML) submitted a request to evaluate an `MLModel`.
- `INPROGRESS` - The evaluation is underway.
- `FAILED` - The request to evaluate an `MLModel` did not run to completion. It is not usable.
- `COMPLETED` - The evaluation process completed successfully.
- `DELETED` - The `Evaluation` is marked as deleted. It is not usable.

#### performanceMetrics

    PerformanceMetrics performanceMetrics

Measurements of how well the `MLModel` performed using observations referenced by the `DataSource`. One of the following metric is returned based on the type of the `MLModel`:

- BinaryAUC: A binary `MLModel` uses the Area Under the Curve (AUC) technique to measure performance.

- RegressionRMSE: A regression `MLModel` uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

- MulticlassAvgFScore: A multiclass `MLModel` uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

#### logUri

    String logUri

A link to the file that contains logs of the `CreateEvaluation` operation.

#### message

    String message

A description of the most recent details about evaluating the `MLModel`.

#### computeTime

    Long computeTime

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the `Evaluation`, normalized and scaled on computation resources. `ComputeTime` is only available if the `Evaluation` is in the `COMPLETED` state.

#### finishedAt

    Date finishedAt

The epoch time when Amazon Machine Learning marked the `Evaluation` as `COMPLETED` or `FAILED`. `FinishedAt` is only available when the `Evaluation` is in the `COMPLETED` or `FAILED` state.

#### startedAt

    Date startedAt

The epoch time when Amazon Machine Learning marked the `Evaluation` as `INPROGRESS`. `StartedAt` isn't available if the `Evaluation` is in the `PENDING` state.

### Class com.amazonaws.services.machinelearning.model.GetMLModelRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The ID assigned to the `MLModel` at creation.

#### verbose

    Boolean verbose

Specifies whether the `GetMLModel` operation should return `Recipe`.

If true, `Recipe` is returned.

If false, `Recipe` is not returned.

### Class com.amazonaws.services.machinelearning.model.GetMLModelResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The MLModel ID, which is same as the `MLModelId` in the request.

#### trainingDataSourceId

    String trainingDataSourceId

The ID of the training `DataSource`.

#### createdByIamUser

    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.

#### createdAt

    Date createdAt

The time that the `MLModel` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `MLModel`. The time is expressed in epoch time.

#### name

    String name

A user-supplied name or description of the `MLModel`.

#### status

    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.

#### sizeInBytes

    Long sizeInBytes

#### endpointInfo

    RealtimeEndpointInfo endpointInfo

The current endpoint of the `MLModel`

#### trainingParameters

    com.amazonaws.internal.SdkInternalMap<K,V> 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.

#### inputDataLocationS3

    String inputDataLocationS3

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

#### mLModelType

    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?"

#### scoreThreshold

    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`.

#### scoreThresholdLastUpdatedAt

    Date scoreThresholdLastUpdatedAt

The time of the most recent edit to the `ScoreThreshold`. The time is expressed in epoch time.

#### logUri

    String logUri

A link to the file that contains logs of the `CreateMLModel` operation.

#### message

    String message

A description of the most recent details about accessing the `MLModel`.

#### computeTime

    Long computeTime

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the `MLModel`, normalized and scaled on computation resources. `ComputeTime` is only available if the `MLModel` is in the `COMPLETED` state.

#### finishedAt

    Date finishedAt

The epoch time when Amazon Machine Learning marked the `MLModel` as `COMPLETED` or `FAILED`. `FinishedAt` is only available when the `MLModel` is in the `COMPLETED` or `FAILED` state.

#### startedAt

    Date startedAt

The epoch time when Amazon Machine Learning marked the `MLModel` as `INPROGRESS`. `StartedAt` isn't available if the `MLModel` is in the `PENDING` state.

#### recipe

    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>Note

This parameter is provided as part of the verbose format.

</note>

#### schema

    String schema

The schema used by all of the data files referenced by the `DataSource`.

<note>Note

This parameter is provided as part of the verbose format.

</note>

### Class com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Serialized Fields

#### code

    Integer code

### Class com.amazonaws.services.machinelearning.model.InternalServerException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Serialized Fields

#### code

    Integer code

### Class com.amazonaws.services.machinelearning.model.InvalidInputException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Serialized Fields

#### code

    Integer code

### Class com.amazonaws.services.machinelearning.model.InvalidTagException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Class com.amazonaws.services.machinelearning.model.LimitExceededException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Serialized Fields

#### code

    Integer code

### Class com.amazonaws.services.machinelearning.model.MLModel extends Object implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The ID assigned to the `MLModel` at creation.

#### trainingDataSourceId

    String trainingDataSourceId

The ID of the training `DataSource`. The `CreateMLModel` operation uses the `TrainingDataSourceId`.

#### createdByIamUser

    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.

#### createdAt

    Date createdAt

The time that the `MLModel` was created. The time is expressed in epoch time.

#### lastUpdatedAt

    Date lastUpdatedAt

The time of the most recent edit to the `MLModel`. The time is expressed in epoch time.

#### name

    String name

A user-supplied name or description of the `MLModel`.

#### status

    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.

#### sizeInBytes

    Long sizeInBytes

#### endpointInfo

    RealtimeEndpointInfo endpointInfo

The current endpoint of the `MLModel`.

#### trainingParameters

    com.amazonaws.internal.SdkInternalMap<K,V> 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.

#### inputDataLocationS3

    String inputDataLocationS3

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

#### algorithm

    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.

#### mLModelType

    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?".

#### scoreThreshold

    Float scoreThreshold

#### scoreThresholdLastUpdatedAt

    Date scoreThresholdLastUpdatedAt

The time of the most recent edit to the `ScoreThreshold`. The time is expressed in epoch time.

#### message

    String message

A description of the most recent details about accessing the `MLModel`.

#### computeTime

    Long computeTime

#### finishedAt

    Date finishedAt

#### startedAt

    Date startedAt

### Class com.amazonaws.services.machinelearning.model.PerformanceMetrics extends Object implements Serializable

### Serialized Fields

#### properties

    com.amazonaws.internal.SdkInternalMap<K,V> properties

### Class com.amazonaws.services.machinelearning.model.Prediction extends Object implements Serializable

### Serialized Fields

#### predictedLabel

    String predictedLabel

The prediction label for either a `BINARY` or `MULTICLASS` `MLModel`.

#### predictedValue

    Float predictedValue

The prediction value for `REGRESSION` `MLModel`.

#### predictedScores

    com.amazonaws.internal.SdkInternalMap<K,V> predictedScores

#### details

    com.amazonaws.internal.SdkInternalMap<K,V> details

### Class com.amazonaws.services.machinelearning.model.PredictorNotMountedException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Class com.amazonaws.services.machinelearning.model.PredictRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

A unique identifier of the `MLModel`.

#### record

    com.amazonaws.internal.SdkInternalMap<K,V> record

#### predictEndpoint

    String predictEndpoint

### Class com.amazonaws.services.machinelearning.model.PredictResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### prediction

    Prediction prediction

### Class com.amazonaws.services.machinelearning.model.RDSDatabase extends Object implements Serializable

### Serialized Fields

#### instanceIdentifier

    String instanceIdentifier

The ID of an RDS DB instance.

#### databaseName

    String databaseName

### Class com.amazonaws.services.machinelearning.model.RDSDatabaseCredentials extends Object implements Serializable

### Serialized Fields

#### username

    String username

#### password

    String password

### Class com.amazonaws.services.machinelearning.model.RDSDataSpec extends Object implements Serializable

### Serialized Fields

#### databaseInformation

    RDSDatabase databaseInformation

Describes the `DatabaseName` and `InstanceIdentifier` of an Amazon RDS database.

#### selectSqlQuery

    String selectSqlQuery

The query that is used to retrieve the observation data for the `DataSource`.

#### databaseCredentials

    RDSDatabaseCredentials databaseCredentials

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

#### s3StagingLocation

    String s3StagingLocation

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using `SelectSqlQuery` is stored in this location.

#### dataRearrangement

    String dataRearrangement

A JSON string that represents the splitting and rearrangement processing to be applied to a `DataSource`. If the `DataRearrangement` parameter is not provided, all of the input data is used to create the `Datasource`.

There are multiple parameters that control what data is used to create a datasource:

- `percentBegin`

  Use `percentBegin` to indicate the beginning of the range of the data used to create the Datasource. If you do not include `percentBegin` and `percentEnd`, Amazon ML includes all of the data when creating the datasource.

- `percentEnd`

  Use `percentEnd` to indicate the end of the range of the data used to create the Datasource. If you do not include `percentBegin` and `percentEnd`, Amazon ML includes all of the data when creating the datasource.

- `complement`

  The `complement` parameter instructs Amazon ML to use the data that is not included in the range of `percentBegin` to `percentEnd` to create a datasource. The `complement` parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for `percentBegin` and `percentEnd`, along with the `complement` parameter.

  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

  Datasource for evaluation: `{"splitting":{"percentBegin":0, "percentEnd":25}}`

  Datasource for training: `{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}`

- `strategy`

  To change how Amazon ML splits the data for a datasource, use the `strategy` parameter.

  The default value for the `strategy` parameter is `sequential`, meaning that Amazon ML takes all of the data records between the `percentBegin` and `percentEnd` parameters for the datasource, in the order that the records appear in the input data.

  The following two `DataRearrangement` lines are examples of sequentially ordered training and evaluation datasources:

  Datasource for evaluation: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}`

  Datasource for training: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}`

  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the `strategy` parameter to `random` and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between `percentBegin` and `percentEnd`. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

  The following two `DataRearrangement` lines are examples of non-sequentially ordered training and evaluation datasources:

  Datasource for evaluation: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}`

  Datasource for training: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}`

#### dataSchema

    String dataSchema

A JSON string that represents the schema for an Amazon RDS `DataSource`. The `DataSchema` defines the structure of the observation data in the data file(s) referenced in the `DataSource`.

A `DataSchema` is not required if you specify a `DataSchemaUri`

Define your `DataSchema` as a series of key-value pairs. `attributes` and `excludedVariableNames` have an array of key-value pairs for their value. Use the following format to define your `DataSchema`.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

#### dataSchemaUri

    String dataSchemaUri

The Amazon S3 location of the `DataSchema`.

#### resourceRole

    String resourceRole

The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.

#### serviceRole

    String serviceRole

The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

#### subnetId

    String subnetId

The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

#### securityGroupIds

    com.amazonaws.internal.SdkInternalList<T> securityGroupIds

The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

### Class com.amazonaws.services.machinelearning.model.RDSMetadata extends Object implements Serializable

### Serialized Fields

#### database

    RDSDatabase database

The database details required to connect to an Amazon RDS.

#### databaseUserName

    String databaseUserName

#### selectSqlQuery

    String selectSqlQuery

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if `Verbose` is true in `GetDataSourceInput`.

#### resourceRole

    String resourceRole

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

#### serviceRole

    String serviceRole

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

#### dataPipelineId

    String dataPipelineId

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

### Class com.amazonaws.services.machinelearning.model.RealtimeEndpointInfo extends Object implements Serializable

### Serialized Fields

#### peakRequestsPerSecond

    Integer peakRequestsPerSecond

The maximum processing rate for the real-time endpoint for `MLModel`, measured in incoming requests per second.

#### createdAt

    Date createdAt

The time that the request to create the real-time endpoint for the `MLModel` was received. The time is expressed in epoch time.

#### endpointUrl

    String endpointUrl

The URI that specifies where to send real-time prediction requests for the `MLModel`.

<note>Note

The application must wait until the real-time endpoint is ready before using this URI.

</note>

#### endpointStatus

    String endpointStatus

The current status of the real-time endpoint for the `MLModel`. This element can have one of the following values:

- `NONE` - Endpoint does not exist or was previously deleted.
- `READY` - Endpoint is ready to be used for real-time predictions.
- `UPDATING` - Updating/creating the endpoint.

### Class com.amazonaws.services.machinelearning.model.RedshiftDatabase extends Object implements Serializable

### Serialized Fields

#### databaseName

    String databaseName

#### clusterIdentifier

    String clusterIdentifier

### Class com.amazonaws.services.machinelearning.model.RedshiftDatabaseCredentials extends Object implements Serializable

### Serialized Fields

#### username

    String username

#### password

    String password

### Class com.amazonaws.services.machinelearning.model.RedshiftDataSpec extends Object implements Serializable

### Serialized Fields

#### databaseInformation

    RedshiftDatabase databaseInformation

Describes the `DatabaseName` and `ClusterIdentifier` for an Amazon Redshift `DataSource`.

#### selectSqlQuery

    String selectSqlQuery

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift `DataSource`.

#### databaseCredentials

    RedshiftDatabaseCredentials databaseCredentials

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

#### s3StagingLocation

    String s3StagingLocation

Describes an Amazon S3 location to store the result set of the `SelectSqlQuery` query.

#### dataRearrangement

    String dataRearrangement

A JSON string that represents the splitting and rearrangement processing to be applied to a `DataSource`. If the `DataRearrangement` parameter is not provided, all of the input data is used to create the `Datasource`.

There are multiple parameters that control what data is used to create a datasource:

- `percentBegin`

  Use `percentBegin` to indicate the beginning of the range of the data used to create the Datasource. If you do not include `percentBegin` and `percentEnd`, Amazon ML includes all of the data when creating the datasource.

- `percentEnd`

  Use `percentEnd` to indicate the end of the range of the data used to create the Datasource. If you do not include `percentBegin` and `percentEnd`, Amazon ML includes all of the data when creating the datasource.

- `complement`

  The `complement` parameter instructs Amazon ML to use the data that is not included in the range of `percentBegin` to `percentEnd` to create a datasource. The `complement` parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for `percentBegin` and `percentEnd`, along with the `complement` parameter.

  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

  Datasource for evaluation: `{"splitting":{"percentBegin":0, "percentEnd":25}}`

  Datasource for training: `{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}`

- `strategy`

  To change how Amazon ML splits the data for a datasource, use the `strategy` parameter.

  The default value for the `strategy` parameter is `sequential`, meaning that Amazon ML takes all of the data records between the `percentBegin` and `percentEnd` parameters for the datasource, in the order that the records appear in the input data.

  The following two `DataRearrangement` lines are examples of sequentially ordered training and evaluation datasources:

  Datasource for evaluation: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}`

  Datasource for training: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}`

  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the `strategy` parameter to `random` and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between `percentBegin` and `percentEnd`. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

  The following two `DataRearrangement` lines are examples of non-sequentially ordered training and evaluation datasources:

  Datasource for evaluation: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}`

  Datasource for training: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}`

#### dataSchema

    String dataSchema

A JSON string that represents the schema for an Amazon Redshift `DataSource`. The `DataSchema` defines the structure of the observation data in the data file(s) referenced in the `DataSource`.

A `DataSchema` is not required if you specify a `DataSchemaUri`.

Define your `DataSchema` as a series of key-value pairs. `attributes` and `excludedVariableNames` have an array of key-value pairs for their value. Use the following format to define your `DataSchema`.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

#### dataSchemaUri

    String dataSchemaUri

Describes the schema location for an Amazon Redshift `DataSource`.

### Class com.amazonaws.services.machinelearning.model.RedshiftMetadata extends Object implements Serializable

### Serialized Fields

#### redshiftDatabase

    RedshiftDatabase redshiftDatabase

#### databaseUserName

    String databaseUserName

#### selectSqlQuery

    String selectSqlQuery

The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if `Verbose` is true in GetDataSourceInput.

### Class com.amazonaws.services.machinelearning.model.ResourceNotFoundException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Serialized Fields

#### code

    Integer code

### Class com.amazonaws.services.machinelearning.model.S3DataSpec extends Object implements Serializable

### Serialized Fields

#### dataLocationS3

    String dataLocationS3

The location of the data file(s) used by a `DataSource`. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

#### dataRearrangement

    String dataRearrangement

A JSON string that represents the splitting and rearrangement processing to be applied to a `DataSource`. If the `DataRearrangement` parameter is not provided, all of the input data is used to create the `Datasource`.

There are multiple parameters that control what data is used to create a datasource:

- `percentBegin`

  Use `percentBegin` to indicate the beginning of the range of the data used to create the Datasource. If you do not include `percentBegin` and `percentEnd`, Amazon ML includes all of the data when creating the datasource.

- `percentEnd`

  Use `percentEnd` to indicate the end of the range of the data used to create the Datasource. If you do not include `percentBegin` and `percentEnd`, Amazon ML includes all of the data when creating the datasource.

- `complement`

  The `complement` parameter instructs Amazon ML to use the data that is not included in the range of `percentBegin` to `percentEnd` to create a datasource. The `complement` parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for `percentBegin` and `percentEnd`, along with the `complement` parameter.

  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

  Datasource for evaluation: `{"splitting":{"percentBegin":0, "percentEnd":25}}`

  Datasource for training: `{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}`

- `strategy`

  To change how Amazon ML splits the data for a datasource, use the `strategy` parameter.

  The default value for the `strategy` parameter is `sequential`, meaning that Amazon ML takes all of the data records between the `percentBegin` and `percentEnd` parameters for the datasource, in the order that the records appear in the input data.

  The following two `DataRearrangement` lines are examples of sequentially ordered training and evaluation datasources:

  Datasource for evaluation: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}`

  Datasource for training: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}`

  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the `strategy` parameter to `random` and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between `percentBegin` and `percentEnd`. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

  The following two `DataRearrangement` lines are examples of non-sequentially ordered training and evaluation datasources:

  Datasource for evaluation: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}`

  Datasource for training: `{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}`

#### dataSchema

    String dataSchema

A JSON string that represents the schema for an Amazon S3 `DataSource`. The `DataSchema` defines the structure of the observation data in the data file(s) referenced in the `DataSource`.

You must provide either the `DataSchema` or the `DataSchemaLocationS3`.

Define your `DataSchema` as a series of key-value pairs. `attributes` and `excludedVariableNames` have an array of key-value pairs for their value. Use the following format to define your `DataSchema`.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

#### dataSchemaLocationS3

    String dataSchemaLocationS3

Describes the schema location in Amazon S3. You must provide either the `DataSchema` or the `DataSchemaLocationS3`.

### Class com.amazonaws.services.machinelearning.model.Tag extends Object implements Serializable

### Serialized Fields

#### key

    String key

A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

#### value

    String value

An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

### Class com.amazonaws.services.machinelearning.model.TagLimitExceededException extends AmazonMachineLearningException implements Serializable

serialVersionUID:
  1L

### Class com.amazonaws.services.machinelearning.model.UpdateBatchPredictionRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

The ID assigned to the `BatchPrediction` during creation.

#### batchPredictionName

    String batchPredictionName

A new user-supplied name or description of the `BatchPrediction`.

### Class com.amazonaws.services.machinelearning.model.UpdateBatchPredictionResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### batchPredictionId

    String batchPredictionId

The ID assigned to the `BatchPrediction` during creation. This value should be identical to the value of the `BatchPredictionId` in the request.

### Class com.amazonaws.services.machinelearning.model.UpdateDataSourceRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

The ID assigned to the `DataSource` during creation.

#### dataSourceName

    String dataSourceName

A new user-supplied name or description of the `DataSource` that will replace the current description.

### Class com.amazonaws.services.machinelearning.model.UpdateDataSourceResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### dataSourceId

    String dataSourceId

The ID assigned to the `DataSource` during creation. This value should be identical to the value of the `DataSourceID` in the request.

### Class com.amazonaws.services.machinelearning.model.UpdateEvaluationRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

The ID assigned to the `Evaluation` during creation.

#### evaluationName

    String evaluationName

A new user-supplied name or description of the `Evaluation` that will replace the current content.

### Class com.amazonaws.services.machinelearning.model.UpdateEvaluationResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### evaluationId

    String evaluationId

The ID assigned to the `Evaluation` during creation. This value should be identical to the value of the `Evaluation` in the request.

### Class com.amazonaws.services.machinelearning.model.UpdateMLModelRequest extends AmazonWebServiceRequest implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The ID assigned to the `MLModel` during creation.

#### mLModelName

    String mLModelName

A user-supplied name or description of the `MLModel`.

#### scoreThreshold

    Float scoreThreshold

The `ScoreThreshold` used in binary classification `MLModel` that marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the `ScoreThreshold` receive a positive result from the `MLModel`, such as `true`. Output values less than the `ScoreThreshold` receive a negative response from the `MLModel`, such as `false`.

### Class com.amazonaws.services.machinelearning.model.UpdateMLModelResult extends AmazonWebServiceResult<ResponseMetadata> implements Serializable

### Serialized Fields

#### mLModelId

    String mLModelId

The ID assigned to the `MLModel` during creation. This value should be identical to the value of the `MLModelID` in the request.