The ID assigned to the DataSource during creation.

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

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

The response's http status code.

A user-supplied ID that uniquely identifies the DataSource.

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

The response's http status code.

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

The type of the ML object.

The ID of the tagged ML object.

The type of the tagged ML object.

A list of tags associated with the ML object.

The response's http status code.

A user-supplied name or description of the DataSource.

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.

A user-supplied ID that uniquely identifies the DataSource.

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}}"

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

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

The response's http status code.

A user-supplied name or description of the DataSource.

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.

A user-supplied identifier that uniquely identifies the DataSource.

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}}"

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

The response's http status code.

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.

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.

A user-supplied name or description of the MLModel.

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.

A user-supplied ID that uniquely identifies the MLModel.

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.

The DataSource that points to the training data.

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

The response's http status code.

One or more tags to delete.

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

The type of the tagged ML object.

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

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

The response's http status code.

A user-supplied ID that uniquely identifies the BatchPrediction.

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

The response's http status code.

The ID assigned to the BatchPrediction during creation.

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

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

The response's http status code.

Specifies whether the GetMLModel operation should return Recipe.

If true, Recipe is returned.

If false, Recipe is not returned.

The ID assigned to the MLModel at creation.

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.

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

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

• sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

• sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
• sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.

• sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

• sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

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

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

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.

The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

Note: This parameter is provided as part of the verbose format.

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

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

Undocumented member.

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

Note: This parameter is provided as part of the verbose format.

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

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.

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.

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.

A user-supplied name or description of the MLModel.

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

The current endpoint of the MLModel

The ID of the training DataSource.

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

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?"

The response's http status code.

Specifies whether the GetDataSource operation should return DataSourceSchema.

If true, DataSourceSchema is returned.

If false, DataSourceSchema is not returned.

The ID assigned to the DataSource at creation.

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.

The number of data files referenced by the DataSource.

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

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

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.

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

Undocumented member.

The total size of observations in the data files.

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

Note: This parameter is provided as part of the verbose format.

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

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.

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.

A user-supplied name or description of the DataSource.

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

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

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

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

Undocumented member.

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

Undocumented member.

The response's http status code.

The ID assigned to the Evaluation during creation.

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

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

The response's http status code.

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

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

The response's http status code.

A user-supplied ID that uniquely identifies the MLModel.

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

The response's http status code.

A user-supplied name or description of the MLModel.

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.

The ID assigned to the MLModel during creation.

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

The response's http status code.

An ID assigned to the BatchPrediction at creation.

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.

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

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

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.

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

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

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

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

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

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

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.

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

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.

A user-supplied name or description of the BatchPrediction.

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

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

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

The response's http status code.

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

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.

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

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

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

An ID of the page in the paginated results.

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

• 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.

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.

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

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.

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.

A list of BatchPrediction objects that meet the search criteria.

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

The response's http status code.

A user-supplied name or description of the DataSource.

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.

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

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}}"

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.

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

The response's http status code.

A user-supplied name or description of the Evaluation.

A user-supplied ID that uniquely identifies the Evaluation.

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.

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

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

The response's http status code.

A unique identifier of the MLModel.

Undocumented member.

Undocumented member.

Undocumented member.

The response's http status code.

The ID assigned to the MLModel during creation.

The endpoint information of the MLModel

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

The response's http status code.

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

A user-supplied ID that uniquely identifies the BatchPrediction.

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

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

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.

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

The response's http status code.

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.

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.

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.

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

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

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.

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

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

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

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.

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.

A user-supplied name or description of the Evaluation.

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

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

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

The DataSource used for this evaluation.

The response's http status code.

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

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.

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

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

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

The ID of the page in the paginated results.

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.

The maximum number of Evaluation to include in the result.

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

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.

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.

A list of Evaluation that meet the search criteria.

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

The response's http status code.

The ID assigned to the MLModel during creation.

The endpoint information of the MLModel

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

The response's http status code.

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.

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

The type of the ML object to tag.

The ID of the ML object that was tagged.

The type of the ML object that was tagged.

The response's http status code.

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

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.

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

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

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

The ID of the page in the paginated results.

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.

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.

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

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.

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.

A list of MLModel that meet the search criteria.

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

The response's http status code.

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

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.

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

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

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

The ID of the page in the paginated results.

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.

The maximum number of DataSource to include in the result.

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

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.

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.

A list of DataSource that meet the search criteria.

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

The response's http status code.

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.

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

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

Undocumented member.

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

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

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

Undocumented member.

Undocumented member.

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

Undocumented member.

Undocumented member.

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.

A user-supplied name or description of the BatchPrediction.

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

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: ':', '//', '/./', '/../'.

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.

The number of data files referenced by the DataSource.

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

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

Undocumented member.

The ID that is assigned to the DataSource during creation.

Undocumented member.

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

Undocumented member.

Undocumented member.

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.

A user-supplied name or description of the DataSource.

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

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

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

Undocumented member.

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

Undocumented member.

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.

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.

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

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

Undocumented member.

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

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

Undocumented member.

Undocumented member.

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.

A user-supplied name or description of the Evaluation.

The ID that is assigned to the Evaluation at creation.

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

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

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.

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

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.

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

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

Undocumented member.

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

The ID assigned to the MLModel at creation.

Undocumented member.

Undocumented member.

Undocumented member.

Undocumented member.

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.

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.

A user-supplied name or description of the MLModel.

The current endpoint of the MLModel.

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

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

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?".

Undocumented member.

The prediction value for REGRESSION MLModel.

The prediction label for either a BINARY or MULTICLASS MLModel.

Undocumented member.

Undocumented member.

The Amazon S3 location of the 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" ] }

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"}}

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

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

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

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

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.

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.

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.

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.

The ID of an RDS DB instance.

Undocumented member.

Undocumented member.

Undocumented member.

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

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.

The database details required to connect to an Amazon RDS.

Undocumented member.

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.

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.

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

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

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

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.

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

Describes the schema location for an Amazon Redshift DataSource.

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" ] }

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"}}

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

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

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

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

Undocumented member.

Undocumented member.

Undocumented member.

Undocumented member.

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

Undocumented member.

Undocumented member.

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" ] }

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

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"}}

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.

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

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