# importances¶

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

Returns the data for the column importances.

Column importances extend the idea of column importances to the columns originally inserted into the pipeline. Each column is assigned an importance value that measures its contribution to the predictive performance. All columns 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 columns.

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