Package software.amazon.awssdk.services.sagemaker.model

Interface AutoMLChannel.Builder

    • Method Detail

      • dataSource

        AutoMLChannel.Builder dataSource​(AutoMLDataSource 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.

      • compressionType

        AutoMLChannel.Builder compressionType​(String compressionType)

        You can use Gzip or None. The default value is None.

        Parameters:
        compressionType - You can use Gzip or None. The default value is None.
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        CompressionType, CompressionType

      • compressionType

        AutoMLChannel.Builder compressionType​(CompressionType compressionType)

        You can use Gzip or None. The default value is None.

        Parameters:
        compressionType - You can use Gzip or None. The default value is None.
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        CompressionType, CompressionType

      • targetAttributeName

        AutoMLChannel.Builder targetAttributeName​(String 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

        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.

        Parameters:
        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.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • channelType

        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.

        Parameters:
        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.
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        AutoMLChannelType, AutoMLChannelType

      • channelType

        AutoMLChannel.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.

        Parameters:
        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.
        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        AutoMLChannelType, AutoMLChannelType

      • sampleWeightAttributeName

        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. 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.