data_describe.core.importance
====================================

.. py:module:: data_describe.core.importance





.. autoapisummary::

   data_describe.core.importance.importance


.. function:: importance(data, target: str, preprocess_func=None, estimator=None, return_values: bool = False, truncate: bool = True, top_features: Optional[int] = None, compute_backend: Optional[str] = None, viz_backend: Optional[str] = None, **kwargs)

   Variable importance chart.

   This feature utilizes fits a simple model to the dataset to generate an estimate
   of feature importance (predictive power). Note that these results are dependent on
   the accuracy of the fitted model and should refined during modeling.

   :param data: A Pandas data frame
   :param target: Name of the response column, as a string
   :param preprocess_func: A custom preprocessing function that takes a Pandas dataframe and the target/response column as a string. Returns X and y as tuple.
   :param estimator: A custom sklearn estimator. Default is Random Forest Classifier
   :param return_values: If True, only the importance values as a numpy array
   :param truncate: If True, negative importance values will be truncated (set to zero)
   :param top_features: Return the top N most important features. Default is None (all features)
   :param compute_backend: The compute backend
   :param viz_backend: The visualization backend
   :param \*\*kwargs: Other arguments to be passed to the preprocess function

   :returns: Matplotlib figure