Module types

Data structures used by operation inputs/outputs.

Modules

• builders
  Builders
• error
  Error types that Amazon Machine Learning can respond with.

Structs

• BatchPrediction

  Represents the output of a GetBatchPrediction operation.

  The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

• DataSource

  Represents the output of the GetDataSource operation.

  The content consists of the detailed metadata and data file information and the current status of the DataSource.

• Evaluation

  Represents the output of GetEvaluation operation.

  The content consists of the detailed metadata and data file information and the current status of the Evaluation.

• MlModel

  Represents the output of a GetMLModel operation.

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

• PerformanceMetrics

  Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

  • BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

  • RegressionRMSE: The 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: The multiclass MLModel uses the F1 score technique to measure performance.

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

• Prediction

  The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD

  • PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.

  • PredictedScores - Contains the raw classification score corresponding to each label.

  • PredictedValue - Present for a REGRESSION MLModel request.

• RdsDataSpec

  The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

• RdsDatabase

  The database details of an Amazon RDS database.

• RdsDatabaseCredentials

  The database credentials to connect to a database on an RDS DB instance.

• RdsMetadata

  The datasource details that are specific to Amazon RDS.

• RealtimeEndpointInfo

  Describes the real-time endpoint information for an MLModel.

• RedshiftDataSpec

  Describes the data specification of an Amazon Redshift DataSource.

• RedshiftDatabase

  Describes the database details required to connect to an Amazon Redshift database.

• RedshiftDatabaseCredentials

  Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

• RedshiftMetadata

  Describes the DataSource details specific to Amazon Redshift.

• S3DataSpec

  Describes the data specification of a DataSource.

• Tag

  A custom key-value pair associated with an ML object, such as an ML model.

Enums

• Algorithm
  When writing a match expression against Algorithm, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• BatchPredictionFilterVariable
  When writing a match expression against BatchPredictionFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DataSourceFilterVariable
  When writing a match expression against DataSourceFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• DetailsAttributes
  When writing a match expression against DetailsAttributes, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• EntityStatus
  When writing a match expression against EntityStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• EvaluationFilterVariable
  When writing a match expression against EvaluationFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• MlModelFilterVariable
  When writing a match expression against MlModelFilterVariable, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• MlModelType
  When writing a match expression against MlModelType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• RealtimeEndpointStatus
  When writing a match expression against RealtimeEndpointStatus, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• SortOrder
  When writing a match expression against SortOrder, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• TaggableResourceType
  When writing a match expression against TaggableResourceType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.