importances

Tables.importances(target_num: int = 0, sort: bool = True) → Tuple[ndarray[Any, dtype[str_]], ndarray[Any, dtype[float64]]]

Returns the importances of tables.

Table importances are calculated by summing up the importances of the columns belonging to the tables. Each column is assigned an importance value that measures its contribution to the predictive performance. For each target, the importances add up to 1.

Args:

target_num (int):

Indicates for which target you want to view the importances. (Pipelines can have more than one target.)

sort (bool):

Whether you want the results to be sorted.

Return:

(numpy.ndarray, numpy.ndarray):

• The first array contains the names of the tables.

• The second array contains their importances. By definition, all importances add up to 1.