vastorbit.machine_learning.vast.pipeline.Pipeline.predict¶
- Pipeline.predict(vdf: Annotated[str | VastFrame, ''] = None, X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str = 'estimator') VastFrame¶
Applies the model on a VastFrame.
- Parameters:
vdf (SQLRelation, optional) – Input VastFrame. You can also specify a customized relation, but you must enclose it with an alias. For example:
(SELECT 1) xis valid whereas(SELECT 1)and “SELECT 1” are invalid.X (SQLColumns, optional) – List of the input VastColumns.
name (str, optional) – Name of the added VastColumn.
- Returns:
object result of the model transformation.
- Return type: