Class: Aws::SageMaker::Types::AutoMLChannel
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::AutoMLChannel
- Includes:
- Aws::Structure
- Defined in:
- lib/aws-sdk-sagemaker/types.rb
Overview
A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see [ Channel].
<note markdown=“1”> A validation dataset must contain the same headers as the training dataset.
</note>
[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_Channel.html
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#channel_type ⇒ String
The channel type (optional) is an ‘enum` string.
-
#compression_type ⇒ String
You can use ‘Gzip` or `None`.
-
#content_type ⇒ String
The content type of the data from the input source.
-
#data_source ⇒ Types::AutoMLDataSource
The data source for an AutoML channel.
-
#sample_weight_attribute_name ⇒ 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.
-
#target_attribute_name ⇒ String
The name of the target variable in supervised learning, usually represented by ‘y’.
Instance Attribute Details
#channel_type ⇒ String
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].
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# File 'lib/aws-sdk-sagemaker/types.rb', line 2086 class AutoMLChannel < Struct.new( :data_source, :compression_type, :target_attribute_name, :content_type, :channel_type, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#compression_type ⇒ String
You can use ‘Gzip` or `None`. The default value is `None`.
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# File 'lib/aws-sdk-sagemaker/types.rb', line 2086 class AutoMLChannel < Struct.new( :data_source, :compression_type, :target_attribute_name, :content_type, :channel_type, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#content_type ⇒ 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`.
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# File 'lib/aws-sdk-sagemaker/types.rb', line 2086 class AutoMLChannel < Struct.new( :data_source, :compression_type, :target_attribute_name, :content_type, :channel_type, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#data_source ⇒ Types::AutoMLDataSource
The data source for an AutoML channel.
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# File 'lib/aws-sdk-sagemaker/types.rb', line 2086 class AutoMLChannel < Struct.new( :data_source, :compression_type, :target_attribute_name, :content_type, :channel_type, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#sample_weight_attribute_name ⇒ 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].
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.
[1]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-metrics-validation.html [2]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html
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# File 'lib/aws-sdk-sagemaker/types.rb', line 2086 class AutoMLChannel < Struct.new( :data_source, :compression_type, :target_attribute_name, :content_type, :channel_type, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |
#target_attribute_name ⇒ String
The name of the target variable in supervised learning, usually represented by ‘y’.
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# File 'lib/aws-sdk-sagemaker/types.rb', line 2086 class AutoMLChannel < Struct.new( :data_source, :compression_type, :target_attribute_name, :content_type, :channel_type, :sample_weight_attribute_name) SENSITIVE = [] include Aws::Structure end |