Pipeline.predict(population_table, peripheral_tables=None, table_name='')[source]

Forecasts on new, unseen data using the trained predictor.

Returns the predictions generated by the pipeline based on population_table and peripheral_tables or writes them into a data base named table_name.

population_table (Union[pandas.DataFrame,]):

Main table corresponding to the population Placeholder instance variable. Its target variable(s) will be ignored.

peripheral_tables (

Union[, List[]]): Additional tables corresponding to the peripheral Placeholder instance variable. They have to be provided in the exact same order as their corresponding placeholders! A single DataFrame will be wrapped into a list internally.

table_name (str, optional):

If not an empty string, the resulting predictions will be written into the database of the same name. See Unified import interface for further information.

IOError: If the pipeline corresponding to the instance

variable name could not be found on the engine or the pipeline could not be fitted.

TypeError: If any input argument is not of proper type. ValueError: If no valid predictor was set. KeyError: If an unsupported instance variable is


TypeError: If any instance variable is of wrong type. ValueError: If any instance variable does not match its

possible choices (string) or is out of the expected bounds (numerical).


Resulting predictions provided in an array of the (number of rows in population_table, number of targets in population_table).


Only fitted pipelines (fit()) can be used for prediction.