multicollinearity_data_check
=========================================================

.. py:module:: evalml.data_checks.multicollinearity_data_check

.. autoapi-nested-parse::

   Data check to check if any set features are likely to be multicollinear.



Module Contents
---------------

Classes Summary
~~~~~~~~~~~~~~~

.. autoapisummary::

   evalml.data_checks.multicollinearity_data_check.MulticollinearityDataCheck





Contents
~~~~~~~~~~~~~~~~~~~
.. py:class:: MulticollinearityDataCheck(threshold=0.9)



   Check if any set features are likely to be multicollinear.

   :param threshold: The threshold to be considered. Defaults to 0.9.
   :type threshold: float




   **Methods**

   .. autoapisummary::
      :nosignatures:

      evalml.data_checks.multicollinearity_data_check.MulticollinearityDataCheck.name
      evalml.data_checks.multicollinearity_data_check.MulticollinearityDataCheck.validate

   .. py:method:: name(cls)

      Return a name describing the data check.


   .. py:method:: validate(self, X, y=None)

      Check if any set of features are likely to be multicollinear.

      :param X: The input features to check.
      :type X: pd.DataFrame
      :param y: The target. Ignored.
      :type y: pd.Series

      :returns: dict with a DataCheckWarning if there are any potentially multicollinear columns.
      :rtype: dict

      .. rubric:: Example

      >>> import pandas as pd

      Columns in X that are highly correlated with each other will be identified using mutual information.

      >>> col = pd.Series([1, 0, 2, 3, 4] * 15)
      >>> X = pd.DataFrame({"col_1": col, "col_2": col * 3})
      >>> y = pd.Series([1, 0, 0, 1, 0] * 15)
      ...
      >>> multicollinearity_check = MulticollinearityDataCheck(threshold=1.0)
      >>> assert multicollinearity_check.validate(X, y) == [
      ...     {
      ...         "message": "Columns are likely to be correlated: [('col_1', 'col_2')]",
      ...         "data_check_name": "MulticollinearityDataCheck",
      ...         "level": "warning",
      ...         "code": "IS_MULTICOLLINEAR",
      ...         "details": {"columns": [("col_1", "col_2")], "rows": None},
      ...         "action_options": []
      ...     }
      ... ]