linreg_ally.multicollinearity
=============================

.. py:module:: linreg_ally.multicollinearity


Functions
---------

.. autoapisummary::

   linreg_ally.multicollinearity.check_multicollinearity


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

.. py:function:: check_multicollinearity(train_df: pandas.DataFrame, threshold=None, vif_only=False)

   Detects multicollinearity in the training dataset by computing the variance inflation factor (‘VIF’) and pairwise Pearson Correlation for each numeric feature.

   :param train_df: Training dataset
   :type train_df: pd.DataFrame
   :param threshold: Minimum threshold of VIF for a feature to be included in the returned dataframe.
                     Default is None.
   :type threshold: int
   :param vif_only: If true, only a dataframe containing the VIF scores will be returned. Otherwise, the correlation chart is also returned.
   :type vif_only: Boolean

   :returns: * *pd.DataFrame*
             * *A dataframe containing the VIF of all numeric features in train_df.*
             * *alt.Chart* -- A chart that shows the pairwise Pearson Correlations of all numeric columns in train_df.

   :raises TypeError: If `train_df` is not a pandas DataFrame.

   .. rubric:: Examples

   >>> from linreg_ally.multicollinearity import check_multicollinearity
   >>> vif_df, corr_chart = check_multicollinearity(train_df)
   >>> vif_df = check_multicollinearity(train_df, threshold = 5, vif_only = True)