{ "version": "2.0", "operations": { "AddTags": "
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags
updates the tag's value.
Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource
. This operation creates a new BatchPrediction
, and uses an MLModel
and the data files referenced by the DataSource
as information sources.
CreateBatchPrediction
is an asynchronous operation. In response to CreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction
status to PENDING
. After the BatchPrediction
completes, Amazon ML sets the status to COMPLETED
.
You can poll for status updates by using the GetBatchPrediction operation and checking the Status
parameter of the result. After the COMPLETED
status appears, the results are available in the location specified by the OutputUri
parameter.
Creates a DataSource
object from an Amazon Relational Database Service (Amazon RDS). A DataSource
references data that can be used to perform CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
status to PENDING
. After the DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in the COMPLETED
or PENDING
state can be used only to perform >CreateMLModel
>, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource
operation response.
Creates a DataSource
from a database hosted on an Amazon Redshift cluster. A DataSource
references data that can be used to perform either CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
status to PENDING
. After the DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
states can be used to perform only CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource
operation response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to transfer the result set of the SelectSqlQuery
query to S3StagingLocation
.
After the DataSource
has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource
to train an MLModel
, the DataSource
also requires a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource
for an existing datasource and copy the values to a CreateDataSource
call. Change the settings that you want to change and make sure that all required fields have the appropriate values.
Creates a DataSource
object. A DataSource
references data that can be used to perform CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
status to PENDING
. After the DataSource
has been created and is ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in the COMPLETED
or PENDING
state can be used to perform only CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource
operation response.
The observation data used in a DataSource
should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource
to train an MLModel
, the DataSource
also needs a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
Creates a new Evaluation
of an MLModel
. An MLModel
is evaluated on a set of observations associated to a DataSource
. Like a DataSource
for an MLModel
, the DataSource
for an Evaluation
contains values for the Target Variable
. The Evaluation
compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel
functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType
: BINARY
, REGRESSION
or MULTICLASS
.
CreateEvaluation
is an asynchronous operation. In response to CreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING
. After the Evaluation
is created and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetEvaluation
operation to check progress of the evaluation during the creation operation.
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only the MLModelName
and the ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
has been created and ready is for use, Amazon ML sets the status to COMPLETED
.
You can use the GetMLModel
operation to check the progress of the MLModel
during the creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can be created by setting ComputeStatistics
to true
in CreateDataSourceFromRDS
, CreateDataSourceFromS3
, or CreateDataSourceFromRedshift
operations.
Creates a real-time endpoint for the MLModel
. The endpoint contains the URI of the MLModel
; that is, the location to send real-time prediction requests for the specified MLModel
.
Assigns the DELETED status to a BatchPrediction
, rendering it unusable.
After using the DeleteBatchPrediction
operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction
changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
Assigns the DELETED status to a DataSource
, rendering it unusable.
After using the DeleteDataSource
operation, you can use the GetDataSource operation to verify that the status of the DataSource
changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
Assigns the DELETED
status to an Evaluation
, rendering it unusable.
After invoking the DeleteEvaluation
operation, you can use the GetEvaluation
operation to verify that the status of the Evaluation
changed to DELETED
.
The results of the DeleteEvaluation
operation are irreversible.
Assigns the DELETED
status to an MLModel
, rendering it unusable.
After using the DeleteMLModel
operation, you can use the GetMLModel
operation to verify that the status of the MLModel
changed to DELETED.
Caution: The result of the DeleteMLModel
operation is irreversible.
Deletes a real time endpoint of an MLModel
.
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
", "DescribeBatchPredictions": "Returns a list of BatchPrediction
operations that match the search criteria in the request.
Returns a list of DataSource
that match the search criteria in the request.
Returns a list of DescribeEvaluations
that match the search criteria in the request.
Returns a list of MLModel
that match the search criteria in the request.
Describes one or more of the tags for your Amazon ML object.
", "GetBatchPrediction": "Returns a BatchPrediction
that includes detailed metadata, status, and data file information for a Batch Prediction
request.
Returns a DataSource
that includes metadata and data file information, as well as the current status of the DataSource
.
GetDataSource
provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.
Returns an Evaluation
that includes metadata as well as the current status of the Evaluation
.
Returns an MLModel
that includes detailed metadata, data source information, and the current status of the MLModel
.
GetMLModel
provides results in normal or verbose format.
Generates a prediction for the observation using the specified ML Model
.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
Updates the BatchPredictionName
of a BatchPrediction
.
You can use the GetBatchPrediction
operation to view the contents of the updated data element.
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents of the updated data element.
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents of the updated data element.
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of the updated data element.
Amazon ML returns the following elements.
", "refs": { } }, "Algorithm": { "base": "The function used to train an MLModel
. Training choices supported by Amazon ML include the following:
SGD
- Stochastic Gradient Descent.RandomForest
- Random forest of decision trees.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. An Amazon Web Service (AWS) user account identifier. The account identifier can be an AWS root account or an AWS Identity and Access Management (IAM) user.
", "refs": { "BatchPrediction$CreatedByIamUser": "The AWS user account that invoked the BatchPrediction
. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The AWS user account from which the DataSource
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
", "GetBatchPredictionOutput$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.
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.
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.
", "GetMLModelOutput$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.
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.
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
.
A list of the variables to use in searching or filtering BatchPrediction
.
CreatedAt
- Sets the search criteria to BatchPrediction
creation date.Status
- Sets the search criteria to BatchPrediction
status.Name
- Sets the search criteria to the contents of 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 Service (Amazon S3) bucket or directory.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.A list of BatchPrediction
objects that meet the search criteria.
The value specified in a filtering condition. The ComparatorValue
becomes the reference value when matching or evaluating data values in filtering and searching functions.
The equal to operator. The BatchPrediction
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The BatchPrediction
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The BatchPrediction
results will have FilterVariable
values that are less than the value specified with LT
.
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
.
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
.
The not equal to operator. The BatchPrediction
results will have FilterVariable
values not equal to the value specified with NE
.
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 equal to operator. The DataSource
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The DataSource
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The DataSource
results will have FilterVariable
values that are less than the value specified with LT
.
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
.
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
.
The not equal to operator. The DataSource
results will have FilterVariable
values not equal to the value specified with NE
.
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 equal to operator. The Evaluation
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The Evaluation
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The Evaluation
results will have FilterVariable
values that are less than the value specified with LT
.
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
.
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
.
The not equal to operator. The Evaluation
results will have FilterVariable
values not equal to the value specified with NE
.
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 equal to operator. The MLModel
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The MLModel
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The MLModel
results will have FilterVariable
values that are less than the value specified with LT
.
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
.
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
.
The not equal to operator. The MLModel
results will have FilterVariable
values not equal to the value specified with NE
.
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 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.
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.
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.
The parameter is true
if statistics need to be generated from the observation data.
The parameter is true
if statistics need to be generated from the observation data.
Represents the output of a CreateBatchPrediction
operation, and is an acknowledgement that Amazon ML received the request.
The CreateBatchPrediction
operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction
operation and checking the Status
parameter of the result.
Represents the output of a CreateDataSourceFromRDS
operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS
> operation is asynchronous. You can poll for updates by using the GetBatchPrediction
operation and checking the Status
parameter. You can inspect the Message
when Status
shows up as FAILED
. You can also check the progress of the copy operation by going to the DataPipeline
console and looking up the pipeline using the pipelineId
from the describe call.
Represents the output of a CreateDataSourceFromRedshift
operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRedshift
operation is asynchronous. You can poll for updates by using the GetBatchPrediction
operation and checking the Status
parameter.
Represents the output of a CreateDataSourceFromS3
operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromS3
operation is asynchronous. You can poll for updates by using the GetBatchPrediction
operation and checking the Status
parameter.
Represents the output of a CreateEvaluation
operation, and is an acknowledgement that Amazon ML received the request.
CreateEvaluation
operation is asynchronous. You can poll for status updates by using the GetEvcaluation
operation and checking the Status
parameter.
Represents the output of a CreateMLModel
operation, and is an acknowledgement that Amazon ML received the request.
The CreateMLModel
operation is asynchronous. You can poll for status updates by using the GetMLModel
operation and checking the Status
parameter.
Represents the output of an CreateRealtimeEndpoint
operation.
The result contains the MLModelId
and the endpoint information for the MLModel
.
The endpoint information includes the URI of the MLModel
; that is, the location to send online prediction requests for the specified MLModel
.
A JSON string that represents the splitting and rearrangement requirement used when this DataSource
was created.
A JSON string that represents the splitting and rearrangement requirement used when this DataSource
was created.
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\"}}
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\"}}
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 schema of a DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
. The DataSource schema is expressed in JSON format.
DataSchema
is not required if you specify a DataSchemaUri
{ \"version\": \"1.0\", \"recordAnnotationFieldName\": \"F1\", \"recordWeightFieldName\": \"F2\", \"targetFieldName\": \"F3\", \"dataFormat\": \"CSV\", \"dataFileContainsHeader\": true, \"variables\": [ { \"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\" ] }
", "refs": { "GetDataSourceOutput$DataSourceSchema": "The schema used by all of the data files of this DataSource
.
This parameter is provided as part of the verbose format.
The schema used by all of the data files referenced by the DataSource
.
This parameter is provided as part of the verbose format.
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\" ] }
", "RedshiftDataSpec$DataSchema": "A JSON string that represents the schema for an Amazon Redshift DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
A DataSchema
is not required if you specify a DataSchemaUri
.
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ \"version\": \"1.0\",
\"recordAnnotationFieldName\": \"F1\",
\"recordWeightFieldName\": \"F2\",
\"targetFieldName\": \"F3\",
\"dataFormat\": \"CSV\",
\"dataFileContainsHeader\": true,
\"attributes\": [
{ \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],
\"excludedVariableNames\": [ \"F6\" ] }
", "S3DataSpec$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\" ] }
" } }, "DataSource": { "base": " 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
.
A list of the variables to use in searching or filtering DataSource
.
CreatedAt
- Sets the search criteria to DataSource
creation date.Status
- Sets the search criteria to DataSource
status.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 variable names should match the variable names in the DataSource
.
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.A list of DataSource
that meet the search criteria.
Represents the output of a DeleteBatchPrediction
operation.
You can use the GetBatchPrediction
operation and check the value of the Status
parameter to see whether a BatchPrediction
is marked as DELETED
.
Represents the output of a DeleteDataSource
operation.
Represents the output of a DeleteEvaluation
operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.
You can use the GetEvaluation
operation and check the value of the Status
parameter to see whether an Evaluation
is marked as DELETED
.
Represents the output of a DeleteMLModel
operation.
You can use the GetMLModel
operation and check the value of the Status
parameter to see whether an MLModel
is marked as DELETED
.
Represents the output of an DeleteRealtimeEndpoint
operation.
The result contains the MLModelId
and the endpoint information for the MLModel
.
Amazon ML returns the following elements.
", "refs": { } }, "DescribeBatchPredictionsInput": { "base": null, "refs": { } }, "DescribeBatchPredictionsOutput": { "base": "Represents the output of a DescribeBatchPredictions
operation. The content is essentially a list of BatchPrediction
s.
Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource
.
Represents the query results from a DescribeEvaluations
operation. The content is essentially a list of Evaluation
.
Represents the output of a DescribeMLModels
operation. The content is essentially a list of MLModel
.
Amazon ML returns the following elements.
", "refs": { } }, "DetailsAttributes": { "base": "Contains the key values ofDetailsMap
: PredictiveModelType
- Indicates the type of the MLModel
. Algorithm
- Indicates the algorithm that was used for the MLModel
.",
"refs": {
"DetailsMap$key": null
}
},
"DetailsMap": {
"base": "Provides any additional details regarding the prediction.",
"refs": {
"Prediction$details": null
}
},
"DetailsValue": {
"base": null,
"refs": {
"DetailsMap$value": null
}
},
"EDPPipelineId": {
"base": null,
"refs": {
"RDSMetadata$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.
" } }, "EDPResourceRole": { "base": null, "refs": { "RDSDataSpec$ResourceRole": "The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
", "RDSMetadata$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.
" } }, "EDPSecurityGroupId": { "base": null, "refs": { "EDPSecurityGroupIds$member": null } }, "EDPSecurityGroupIds": { "base": null, "refs": { "RDSDataSpec$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.
" } }, "EDPServiceRole": { "base": null, "refs": { "RDSDataSpec$ServiceRole": "The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
", "RDSMetadata$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.
" } }, "EDPSubnetId": { "base": null, "refs": { "RDSDataSpec$SubnetId": "The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
" } }, "EntityId": { "base": null, "refs": { "AddTagsInput$ResourceId": "The ID of the ML object to tag. For example, exampleModelId
.
The ID of the ML object that was tagged.
", "BatchPrediction$BatchPredictionId": "The ID assigned to the BatchPrediction
at creation. This value should be identical to the value of the BatchPredictionID
in the request.
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.
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.
A user-supplied ID that uniquely identifies the BatchPrediction
. This value is identical to the value of the BatchPredictionId
in the request.
A user-supplied ID that uniquely identifies the DataSource
. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource
.
A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID
in the request.
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.
A user-supplied identifier 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.
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.
A user-supplied ID that uniquely identifies the MLModel
.
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 ID assigned to the MLModel
during creation.
A user-supplied ID that uniquely identifies the MLModel
. This value should be identical to the value of the MLModelId
in the request.
The ID that is assigned to the DataSource
during creation.
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.
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.
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.
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 ID assigned to the MLModel
during creation.
A user-supplied ID that uniquely identifies the MLModel
. This value should be identical to the value of the MLModelId
in the request.
The ID of the tagged ML object. For example, exampleModelId
.
The ID of the ML object from which tags were deleted.
", "DescribeTagsInput$ResourceId": "The ID of the ML object. For example, exampleModelId
.
The ID of the tagged ML object.
", "Evaluation$EvaluationId": "The ID that is assigned to the Evaluation
at creation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the DataSource
that is used to evaluate the MLModel
.
An ID assigned to the BatchPrediction
at creation.
An ID assigned to the BatchPrediction
at creation. This value should be identical to the value of the BatchPredictionID
in the request.
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 ID assigned to the DataSource
at creation.
The ID assigned to the DataSource
at creation. This value should be identical to the value of the DataSourceId
in the request.
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 evaluation ID which is same as the EvaluationId
in the request.
The ID of the MLModel
that was the focus of the evaluation.
The DataSource
used for this evaluation.
The ID assigned to the MLModel
at creation.
The MLModel ID, which is same as the MLModelId
in the request.
The ID of the training DataSource
.
The ID assigned to the MLModel
at creation.
The ID of the training DataSource
. The CreateMLModel
operation uses the TrainingDataSourceId
.
A unique identifier of the MLModel
.
The ID assigned to the BatchPrediction
during creation.
The ID assigned to the BatchPrediction
during creation. This value should be identical to the value of the BatchPredictionId
in the request.
The ID assigned to the DataSource
during creation.
The ID assigned to the DataSource
during creation. This value should be identical to the value of the DataSourceID
in the request.
The ID assigned to the Evaluation
during creation.
The ID assigned to the Evaluation
during creation. This value should be identical to the value of the Evaluation
in the request.
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.
A user-supplied name or description of the Amazon ML resource.
", "refs": { "BatchPrediction$Name": "A user-supplied name or description of the BatchPrediction
.
A user-supplied name or description of the BatchPrediction
. BatchPredictionName
can only use the UTF-8 character set.
A user-supplied name or description of the DataSource
.
A user-supplied name or description of the DataSource
.
A user-supplied name or description of the DataSource
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the MLModel
.
A user-supplied name or description of the DataSource
.
A user-supplied name or description of the Evaluation
.
A user-supplied name or description of the BatchPrediction
.
A user-supplied name or description of the DataSource
.
A user-supplied name or description of the Evaluation
.
A new user-supplied name or description of the BatchPrediction
.
A new user-supplied name or description of the DataSource
that will replace the current description.
A new user-supplied name or description of the Evaluation
that will replace the current content.
A user-supplied name or description of the MLModel
.
Object status with the following possible values:
PENDING
INPROGRESS
FAILED
COMPLETED
DELETED
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 current status of the DataSource
. This element can have one of the following values:
DataSource
.DataSource
did not run to completion. It is not usable.DataSource
is marked as deleted. It is not usable.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.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 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 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.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 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.A timestamp represented in epoch time.
", "refs": { "BatchPrediction$CreatedAt": "The time that the BatchPrediction
was created. The time is expressed in epoch time.
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.
The time of the most recent edit to the BatchPrediction
. The time is expressed in epoch time.
The time that the Evaluation
was created. The time is expressed in epoch time.
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
The time when the BatchPrediction
was created. The time is expressed in epoch time.
The time of the most recent edit to BatchPrediction
. The time is expressed in epoch time.
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 epoch time when Amazon Machine Learning marked the BatchPrediction
as INPROGRESS
. StartedAt
isn't available if the BatchPrediction
is in the PENDING
state.
The time that the DataSource
was created. The time is expressed in epoch time.
The time of the most recent edit to the DataSource
. The time is expressed in epoch time.
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 epoch time when Amazon Machine Learning marked the DataSource
as INPROGRESS
. StartedAt
isn't available if the DataSource
is in the PENDING
state.
The time that the Evaluation
was created. The time is expressed in epoch time.
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
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 epoch time when Amazon Machine Learning marked the Evaluation
as INPROGRESS
. StartedAt
isn't available if the Evaluation
is in the PENDING
state.
The time that the MLModel
was created. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
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 epoch time when Amazon Machine Learning marked the MLModel
as INPROGRESS
. StartedAt
isn't available if the MLModel
is in the PENDING
state.
The time that the MLModel
was created. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
The time that the request to create the real-time endpoint for the MLModel
was received. The time is expressed in epoch time.
Represents the output of GetEvaluation
operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation
.
A list of the variables to use in searching or filtering Evaluation
.
CreatedAt
- Sets the search criteria to Evaluation
creation date.Status
- Sets the search criteria to 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 Predictor
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 Service (Amazon S3) bucket or directory.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.A list of Evaluation
that meet the search criteria.
Represents the output of a GetBatchPrediction
operation and describes a BatchPrediction
.
Represents the output of a GetDataSource
operation and describes a DataSource
.
Represents the output of a GetEvaluation
operation and describes an Evaluation
.
Represents the output of a GetMLModel
operation, and provides detailed information about a MLModel
.
A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
", "refs": { } }, "IntegerType": { "base": "Integer type that is a 32-bit signed number.
", "refs": { "RealtimeEndpointInfo$PeakRequestsPerSecond": " The maximum processing rate for the real-time endpoint for MLModel
, measured in incoming requests per second.
An error on the server occurred when trying to process a request.
", "refs": { } }, "InvalidInputException": { "base": "An error on the client occurred. Typically, the cause is an invalid input value.
", "refs": { } }, "InvalidTagException": { "base": null, "refs": { } }, "Label": { "base": null, "refs": { "Prediction$predictedLabel": "The prediction label for either a BINARY
or MULTICLASS
MLModel
.
The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource
.
Long integer type that is a 64-bit signed number.
", "refs": { "BatchPrediction$ComputeTime": null, "BatchPrediction$TotalRecordCount": null, "BatchPrediction$InvalidRecordCount": null, "DataSource$DataSizeInBytes": "The total number of observations contained in the data files that the DataSource
references.
The number of data files referenced by the DataSource
.
The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction
, normalized and scaled on computation resources. ComputeTime
is only available if the BatchPrediction
is in the COMPLETED
state.
The number of total records that Amazon Machine Learning saw while processing the BatchPrediction
.
The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction
.
The total size of observations in the data files.
", "GetDataSourceOutput$NumberOfFiles": "The number of data files referenced by the DataSource
.
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 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 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.
Represents the output of a GetMLModel
operation.
The content consists of the detailed metadata and the current status of the MLModel
.
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.A user-supplied name or description of the MLModel
.
A user-supplied name or description of the MLModel
.
The category of supervised learning that this MLModel
will address. Choose from the following types:
REGRESSION
if the MLModel
will be used to predict a numeric value.BINARY
if the MLModel
result has two possible values.MULTICLASS
if the MLModel
result has a limited number of values. For more information, see the Amazon Machine Learning Developer Guide.
", "GetMLModelOutput$MLModelType": "Identifies the MLModel
category. The following are the available types:
Identifies the MLModel
category. The following are the available types:
REGRESSION
- Produces a numeric result. For example, \"What price should a house be listed at?\"BINARY
- Produces one of two possible results. For example, \"Is this a child-friendly web site?\".MULTICLASS
- Produces one of several possible results. For example, \"Is this a HIGH-, LOW-, or MEDIUM-risk trade?\".A list of MLModel
that meet the search criteria.
Description of the most recent details about an object.
", "refs": { "BatchPrediction$Message": "A description of the most recent details about processing the batch prediction request.
", "DataSource$Message": "A description of the most recent details about creating the DataSource
.
A description of the most recent details about evaluating the MLModel
.
A description of the most recent details about processing the batch prediction request.
", "GetDataSourceOutput$Message": "The user-supplied description of the most recent details about creating the DataSource
.
A description of the most recent details about evaluating the MLModel
.
A description of the most recent details about accessing the MLModel
.
A description of the most recent details about accessing the MLModel
.
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 maximum number of DataSource
to include in the result.
The maximum number of Evaluation
to include in the result.
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
.
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.
", "refs": { "Evaluation$PerformanceMetrics": "Measurements of how well the MLModel
performed, using observations referenced by the DataSource
. One of the following metrics is returned, based on the type of the MLModel
:
BinaryAUC: A binary MLModel
uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression MLModel
uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
", "GetEvaluationOutput$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.
" } }, "PerformanceMetricsProperties": { "base": null, "refs": { "PerformanceMetrics$Properties": null } }, "PerformanceMetricsPropertyKey": { "base": null, "refs": { "PerformanceMetricsProperties$key": null } }, "PerformanceMetricsPropertyValue": { "base": null, "refs": { "PerformanceMetricsProperties$value": null } }, "PredictInput": { "base": null, "refs": { } }, "PredictOutput": { "base": null, "refs": { } }, "Prediction": { "base": "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.
The exception is thrown when a predict request is made to an unmounted MLModel
.
A link to the file that contains logs of the CreateBatchPrediction
operation.
A link to the file containing logs of CreateDataSourceFrom*
operations.
A link to the file that contains logs of the CreateEvaluation
operation.
A link to the file that contains logs of the CreateMLModel
operation.
The data specification of an Amazon Relational Database Service (Amazon RDS) 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 database details of an Amazon RDS database.
", "refs": { "RDSDataSpec$DatabaseInformation": "Describes the DatabaseName
and InstanceIdentifier
of an Amazon RDS database.
The database details required to connect to an Amazon RDS.
" } }, "RDSDatabaseCredentials": { "base": "The database credentials to connect to a database on an RDS DB instance.
", "refs": { "RDSDataSpec$DatabaseCredentials": "The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
" } }, "RDSDatabaseName": { "base": "The name of a database hosted on an RDS DB instance.
", "refs": { "RDSDatabase$DatabaseName": null } }, "RDSDatabasePassword": { "base": "The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the RDSSelectQuery
query.
The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery
query.
The ID of an RDS DB instance.
" } }, "RDSMetadata": { "base": "The datasource details that are specific to Amazon RDS.
", "refs": { "DataSource$RDSMetadata": null, "GetDataSourceOutput$RDSMetadata": null } }, "RDSSelectSqlQuery": { "base": "The SQL query to be executed against the Amazon RDS database. The SQL query should be valid for the Amazon RDS type being used.
", "refs": { "RDSDataSpec$SelectSqlQuery": "The query that is used to retrieve the observation data for the DataSource
.
The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose
is true in GetDataSourceInput
.
Describes the real-time endpoint information for an MLModel
.
The endpoint information of the MLModel
The endpoint information of the MLModel
The current endpoint of the MLModel
The current endpoint of the MLModel
.
The current status of the real-time endpoint for the MLModel
. This element can have one of the following values:
NONE
- Endpoint does not exist or was previously deleted.READY
- Endpoint is ready to be used for real-time predictions.UPDATING
- Updating/creating the endpoint. The data recipe for creating the MLModel
. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
The recipe to use when training the MLModel
. The Recipe
provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.
This parameter is provided as part of the verbose format.
A map of variable name-value pairs that represent an observation.
", "refs": { "PredictInput$Record": null } }, "RedshiftClusterIdentifier": { "base": "The ID of an Amazon Redshift cluster.
", "refs": { "RedshiftDatabase$ClusterIdentifier": null } }, "RedshiftDataSpec": { "base": "Describes the data specification of an Amazon Redshift DataSource
.
The data specification of an Amazon Redshift DataSource
:
DatabaseInformation -
DatabaseName
- The name of the Amazon Redshift database. ClusterIdentifier
- The unique ID for the Amazon Redshift cluster.DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.
SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource
.
S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery
query is stored in this location.
DataSchemaUri - The Amazon S3 location of the DataSchema
.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri
is specified.
DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the DataSource
.
Sample - \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
Describes the database details required to connect to an Amazon Redshift database.
", "refs": { "RedshiftDataSpec$DatabaseInformation": "Describes the DatabaseName
and ClusterIdentifier
for an Amazon Redshift DataSource
.
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
", "refs": { "RedshiftDataSpec$DatabaseCredentials": "Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
" } }, "RedshiftDatabaseName": { "base": "The name of a database hosted on an Amazon Redshift cluster.
", "refs": { "RedshiftDatabase$DatabaseName": null } }, "RedshiftDatabasePassword": { "base": "A password to be used by Amazon ML to connect to a database on an Amazon Redshift cluster. The password should have sufficient permissions to execute a RedshiftSelectSqlQuery
query. The password should be valid for an Amazon Redshift USER.
A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery
query. The username should be valid for an Amazon Redshift USER.
Describes the DataSource
details specific to Amazon Redshift.
Describes the SQL query to execute on the Amazon Redshift database. The SQL query should be valid for an Amazon Redshift SELECT.
", "refs": { "RedshiftDataSpec$SelectSqlQuery": "Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource
.
The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose
is true in GetDataSourceInput.
A specified resource cannot be located.
", "refs": { } }, "RoleARN": { "base": "The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename.
", "refs": { "CreateDataSourceFromRDSInput$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.
", "CreateDataSourceFromRedshiftInput$RoleARN": "
A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:
A security group to allow Amazon ML to execute the SelectSqlQuery
query on an Amazon Redshift cluster
An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation
Describes the data specification of a DataSource
.
The data specification of a DataSource
:
DataLocationS3 - The Amazon S3 location of the observation data.
DataSchemaLocationS3 - The Amazon S3 location of the DataSchema
.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri
is specified.
DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource
.
Sample - \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
A reference to a file or bucket on Amazon Simple Storage Service (Amazon S3).
", "refs": { "BatchPrediction$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "BatchPrediction$OutputUri": "The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key
portion of the outputURI
field: ':', '//', '/./', '/../'.
The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key
portion of the outputURI
field: ':', '//', '/./', '/../'.
Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the Amazon Machine Learning Developer Guide.
", "CreateMLModelInput$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.
The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource
.
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
", "GetBatchPredictionOutput$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "GetBatchPredictionOutput$OutputUri": "The location of an Amazon S3 bucket or directory to receive the operation results.
", "GetDataSourceOutput$DataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "GetEvaluationOutput$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "GetMLModelOutput$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "MLModel$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "RDSDataSpec$S3StagingLocation": "The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery
is stored in this location.
The Amazon S3 location of the DataSchema
.
Describes an Amazon S3 location to store the result set of the SelectSqlQuery
query.
Describes the schema location for an Amazon Redshift DataSource
.
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.
Describes the schema location in Amazon S3. You must provide either the DataSchema
or the DataSchemaLocationS3
.
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 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 sort order specified in a listing condition. Possible values include the following:
asc
- Present the information in ascending order (from A-Z).dsc
- Present the information in descending order (from Z-A).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
.
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
.
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
.
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
.
String type.
", "refs": { "DescribeBatchPredictionsInput$NextToken": "An ID of the page in the paginated results.
", "DescribeBatchPredictionsOutput$NextToken": "The ID of the next page in the paginated results that indicates at least one more page follows.
", "DescribeDataSourcesInput$NextToken": "The ID of the page in the paginated results.
", "DescribeDataSourcesOutput$NextToken": "An ID of the next page in the paginated results that indicates at least one more page follows.
", "DescribeEvaluationsInput$NextToken": "The ID of the page in the paginated results.
", "DescribeEvaluationsOutput$NextToken": "The ID of the next page in the paginated results that indicates at least one more page follows.
", "DescribeMLModelsInput$NextToken": "The ID of the page in the paginated results.
", "DescribeMLModelsOutput$NextToken": "The ID of the next page in the paginated results that indicates at least one more page follows.
", "TrainingParameters$key": null, "TrainingParameters$value": null } }, "Tag": { "base": "A custom key-value pair associated with an ML object, such as an ML model.
", "refs": { "TagList$member": null } }, "TagKey": { "base": null, "refs": { "Tag$Key": "A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
", "TagKeyList$member": null } }, "TagKeyList": { "base": null, "refs": { "DeleteTagsInput$TagKeys": "One or more tags to delete.
" } }, "TagLimitExceededException": { "base": null, "refs": { } }, "TagList": { "base": null, "refs": { "AddTagsInput$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.
", "DescribeTagsOutput$Tags": "A list of tags associated with the ML object.
" } }, "TagValue": { "base": null, "refs": { "Tag$Value": "An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.
" } }, "TaggableResourceType": { "base": null, "refs": { "AddTagsInput$ResourceType": "The type of the ML object to tag.
", "AddTagsOutput$ResourceType": "The type of the ML object that was tagged.
", "DeleteTagsInput$ResourceType": "The type of the tagged ML object.
", "DeleteTagsOutput$ResourceType": "The type of the ML object from which tags were deleted.
", "DescribeTagsInput$ResourceType": "The type of the ML object.
", "DescribeTagsOutput$ResourceType": "The type of the tagged ML object.
" } }, "TrainingParameters": { "base": null, "refs": { "CreateMLModelInput$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.
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.
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.
Represents the output of an UpdateBatchPrediction
operation.
You can see the updated content by using the GetBatchPrediction
operation.
Represents the output of an UpdateDataSource
operation.
You can see the updated content by using the GetBatchPrediction
operation.
Represents the output of an UpdateEvaluation
operation.
You can see the updated content by using the GetEvaluation
operation.
Represents the output of an UpdateMLModel
operation.
You can see the updated content by using the GetMLModel
operation.
The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags.
", "refs": { "Record$key": null } }, "VariableValue": { "base": "The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values.
", "refs": { "Record$value": null } }, "Verbose": { "base": "Specifies whether a describe operation should return exhaustive or abbreviated information.
", "refs": { "GetDataSourceInput$Verbose": "Specifies whether the GetDataSource
operation should return DataSourceSchema
.
If true, DataSourceSchema
is returned.
If false, DataSourceSchema
is not returned.
Specifies whether the GetMLModel
operation should return Recipe
.
If true, Recipe
is returned.
If false, Recipe
is not returned.
The URI that specifies where to send real-time prediction requests for the MLModel
.
The application must wait until the real-time endpoint is ready before using this URI.
REGRESSION
MLModel
."
}
}
}
}