Interface AutoMLChannel.Builder
- All Superinterfaces:
Buildable
,CopyableBuilder<AutoMLChannel.Builder,
,AutoMLChannel> SdkBuilder<AutoMLChannel.Builder,
,AutoMLChannel> SdkPojo
- Enclosing class:
AutoMLChannel
-
Method Summary
Modifier and TypeMethodDescriptionchannelType
(String channelType) The channel type (optional) is anenum
string.channelType
(AutoMLChannelType channelType) The channel type (optional) is anenum
string.compressionType
(String compressionType) You can useGzip
orNone
.compressionType
(CompressionType compressionType) You can useGzip
orNone
.contentType
(String contentType) The content type of the data from the input source.default AutoMLChannel.Builder
dataSource
(Consumer<AutoMLDataSource.Builder> dataSource) The data source for an AutoML channel.dataSource
(AutoMLDataSource dataSource) The data source for an AutoML channel.sampleWeightAttributeName
(String sampleWeightAttributeName) If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.targetAttributeName
(String targetAttributeName) The name of the target variable in supervised learning, usually represented by 'y'.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFieldNameToField, sdkFields
-
Method Details
-
dataSource
The data source for an AutoML channel.
- Parameters:
dataSource
- The data source for an AutoML channel.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
dataSource
The data source for an AutoML channel.
This is a convenience method that creates an instance of theAutoMLDataSource.Builder
avoiding the need to create one manually viaAutoMLDataSource.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed todataSource(AutoMLDataSource)
.- Parameters:
dataSource
- a consumer that will call methods onAutoMLDataSource.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
compressionType
You can use
Gzip
orNone
. The default value isNone
.- Parameters:
compressionType
- You can useGzip
orNone
. The default value isNone
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
compressionType
You can use
Gzip
orNone
. The default value isNone
.- Parameters:
compressionType
- You can useGzip
orNone
. The default value isNone
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
targetAttributeName
The name of the target variable in supervised learning, usually represented by 'y'.
- Parameters:
targetAttributeName
- The name of the target variable in supervised learning, usually represented by 'y'.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
contentType
The content type of the data from the input source. You can use
text/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.- Parameters:
contentType
- The content type of the data from the input source. You can usetext/csv;header=present
orx-application/vnd.amazon+parquet
. The default value istext/csv;header=present
.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
channelType
The channel type (optional) is an
enum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, see How to specify training and validation datasets.- Parameters:
channelType
- The channel type (optional) is anenum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, see How to specify training and validation datasets.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
channelType
The channel type (optional) is an
enum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, see How to specify training and validation datasets.- Parameters:
channelType
- The channel type (optional) is anenum
string. The default value istraining
. Channels for training and validation must share the sameContentType
andTargetAttributeName
. For information on specifying training and validation channel types, see How to specify training and validation datasets.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
sampleWeightAttributeName
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
- Parameters:
sampleWeightAttributeName
- If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-