Interface AutoMLChannel.Builder
- All Superinterfaces:
Buildable,CopyableBuilder<AutoMLChannel.Builder,,AutoMLChannel> SdkBuilder<AutoMLChannel.Builder,,AutoMLChannel> SdkPojo
- Enclosing class:
AutoMLChannel
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Method Summary
Modifier and TypeMethodDescriptionchannelType(String channelType) The channel type (optional) is anenumstring.channelType(AutoMLChannelType channelType) The channel type (optional) is anenumstring.compressionType(String compressionType) You can useGziporNone.compressionType(CompressionType compressionType) You can useGziporNone.contentType(String contentType) The content type of the data from the input source.default AutoMLChannel.BuilderdataSource(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
copyMethods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, buildMethods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
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Method Details
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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.
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dataSource
The data source for an AutoML channel.
This is a convenience method that creates an instance of theAutoMLDataSource.Builderavoiding the need to create one manually viaAutoMLDataSource.builder().When the
Consumercompletes,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:
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compressionType
You can use
GziporNone. The default value isNone.- Parameters:
compressionType- You can useGziporNone. The default value isNone.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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compressionType
You can use
GziporNone. The default value isNone.- Parameters:
compressionType- You can useGziporNone. The default value isNone.- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
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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.
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contentType
The content type of the data from the input source. You can use
text/csv;header=presentorx-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=presentorx-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.
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channelType
The channel type (optional) is an
enumstring. The default value istraining. Channels for training and validation must share the sameContentTypeandTargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.- Parameters:
channelType- The channel type (optional) is anenumstring. The default value istraining. Channels for training and validation must share the sameContentTypeandTargetAttributeName. 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:
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channelType
The channel type (optional) is an
enumstring. The default value istraining. Channels for training and validation must share the sameContentTypeandTargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.- Parameters:
channelType- The channel type (optional) is anenumstring. The default value istraining. Channels for training and validation must share the sameContentTypeandTargetAttributeName. 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:
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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.
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