public static interface AutoMLChannel.Builder extends SdkPojo, CopyableBuilder<AutoMLChannel.Builder,AutoMLChannel>
| Modifier and Type | Method and Description |
|---|---|
AutoMLChannel.Builder |
channelType(AutoMLChannelType channelType)
The channel type (optional) is an
enum string. |
AutoMLChannel.Builder |
channelType(String channelType)
The channel type (optional) is an
enum string. |
AutoMLChannel.Builder |
compressionType(CompressionType compressionType)
You can use
Gzip or None. |
AutoMLChannel.Builder |
compressionType(String compressionType)
You can use
Gzip or None. |
AutoMLChannel.Builder |
contentType(String contentType)
The content type of the data from the input source.
|
AutoMLChannel.Builder |
dataSource(AutoMLDataSource dataSource)
The data source for an AutoML channel.
|
default AutoMLChannel.Builder |
dataSource(Consumer<AutoMLDataSource.Builder> dataSource)
The data source for an AutoML channel.
|
AutoMLChannel.Builder |
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.
|
AutoMLChannel.Builder |
targetAttributeName(String targetAttributeName)
The name of the target variable in supervised learning, usually represented by 'y'.
|
equalsBySdkFields, sdkFieldscopyapplyMutation, buildAutoMLChannel.Builder dataSource(AutoMLDataSource dataSource)
The data source for an AutoML channel.
dataSource - The data source for an AutoML channel.default AutoMLChannel.Builder dataSource(Consumer<AutoMLDataSource.Builder> 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 via AutoMLDataSource.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to dataSource(AutoMLDataSource).
dataSource - a consumer that will call methods on AutoMLDataSource.BuilderdataSource(AutoMLDataSource)AutoMLChannel.Builder compressionType(String compressionType)
You can use Gzip or None. The default value is None.
compressionType - You can use Gzip or None. The default value is None.CompressionType,
CompressionTypeAutoMLChannel.Builder compressionType(CompressionType compressionType)
You can use Gzip or None. The default value is None.
compressionType - You can use Gzip or None. The default value is None.CompressionType,
CompressionTypeAutoMLChannel.Builder targetAttributeName(String targetAttributeName)
The name of the target variable in supervised learning, usually represented by 'y'.
targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.AutoMLChannel.Builder contentType(String contentType)
The content type of the data from the input source. You can use text/csv;header=present or
x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
contentType - The content type of the data from the input source. You can use text/csv;header=present
or x-application/vnd.amazon+parquet. The default value is
text/csv;header=present.AutoMLChannel.Builder channelType(String channelType)
The channel type (optional) is an enum string. The default value is training.
Channels for training and validation must share the same ContentType and
TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
channelType - The channel type (optional) is an enum string. The default value is training
. Channels for training and validation must share the same ContentType and
TargetAttributeName. For information on specifying training and validation channel types,
see How to specify training and validation datasets.AutoMLChannelType,
AutoMLChannelTypeAutoMLChannel.Builder channelType(AutoMLChannelType channelType)
The channel type (optional) is an enum string. The default value is training.
Channels for training and validation must share the same ContentType and
TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
channelType - The channel type (optional) is an enum string. The default value is training
. Channels for training and validation must share the same ContentType and
TargetAttributeName. For information on specifying training and validation channel types,
see How to specify training and validation datasets.AutoMLChannelType,
AutoMLChannelTypeAutoMLChannel.Builder 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. 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.
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.
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