TableVertical Class

Abstract class for AutoML tasks that use table dataset as input - such as Classification/Regression/Forecasting.

Constructor

TableVertical(*, cv_split_column_names: List[str] | None = None, featurization_settings: _models.TableVerticalFeaturizationSettings | None = None, limit_settings: _models.TableVerticalLimitSettings | None = None, n_cross_validations: _models.NCrossValidations | None = None, test_data: _models.MLTableJobInput | None = None, test_data_size: float | None = None, validation_data: _models.MLTableJobInput | None = None, validation_data_size: float | None = None, weight_column_name: str | None = None, **kwargs: Any)

Keyword-Only Parameters

Name	Description
cv_split_column_names	list[str] Columns to use for CVSplit data. Default value: None
featurization_settings	TableVerticalFeaturizationSettings Featurization inputs needed for AutoML job. Default value: None
limit_settings	TableVerticalLimitSettings Execution constraints for AutoMLJob. Default value: None
n_cross_validations	NCrossValidations Number of cross validation folds to be applied on training dataset when validation dataset is not provided. Default value: None
test_data	MLTableJobInput Test data input. Default value: None
test_data_size	float The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. Default value: None
validation_data	MLTableJobInput Validation data inputs. Default value: None
validation_data_size	float The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. Default value: None
weight_column_name	str The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. Default value: None

Variables

Name	Description
cv_split_column_names	list[str] Columns to use for CVSplit data.
featurization_settings	TableVerticalFeaturizationSettings Featurization inputs needed for AutoML job.
limit_settings	TableVerticalLimitSettings Execution constraints for AutoMLJob.
n_cross_validations	NCrossValidations Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
test_data	MLTableJobInput Test data input.
test_data_size	float The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
validation_data	MLTableJobInput Validation data inputs.
validation_data_size	float The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weight_column_name	str The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.