sagemaker/aws.sdk.kotlin.services.sagemaker.model/AutoMlChannel/Builder

Builder

Properties

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.

compressionType

var compressionType: CompressionType?

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

contentType

var contentType: String?

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.

dataSource

var dataSource: AutoMlDataSource?

The data source for an AutoML channel.

sampleWeightAttributeName

var sampleWeightAttributeName: String?

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.

targetAttributeName

var targetAttributeName: String?

The name of the target variable in supervised learning, usually represented by 'y'.

Functions

dataSource

fun dataSource(block: AutoMlDataSource.Builder.() -> Unit)

construct an aws.sdk.kotlin.services.sagemaker.model.AutoMlDataSource inside the given block