reword doscstrings and user guide psi

docs/user_guide/selection/DropHighPSIFeatures.rst

@@ -54,11 +54,16 @@ Important
 
 When working with the PSI it is worth highlighting the following:
 
-- The PSI is not symmetric; switching the order of the basis and test dataframes in the PSI calculation will lead to different values.
 - The number of bins used to define the distributions has an impact on the PSI values.
 - The PSI is a suitable metric for numerical features (i.e., either continuous or with high cardinality).
 - For categorical or discrete features, the change in distributions is better assessed with Chi-squared.
 
+he PSI is symmetric. That means that switching the order of the basis and test dataframes
+in the PSI calculation will lead to identical values. However, in this implementation, the interval
+limits used to calculate the PSI are inferred from the basis dataframe. Hence, switching the
+order of the dataframes will lead to different interval limits, which in turn may result in
+different PSI values.
+
 Threshold
 ~~~~~~~~~
 

feature_engine/selection/drop_psi_features.py

@@ -171,8 +171,9 @@ class DropHighPSIFeatures(BaseSelector):
 
     switch: boolean, default=False.
         If True, the order of the 2 dataframes used to determine the PSI (basis and
-        test) will be switched. This is important because the PSI is not symmetric,
-        i.e., PSI(a, b) != PSI(b, a)).
+        test) will be switched. This is important because the interval limits used to
+        calculate the PSI are inferred from the basis dataframe. Hence, changing the
+        order of the dataframes may lead to different PSI values.
 
     threshold: float, str, default = 0.25.
         The threshold to drop a feature. If the PSI for a feature is >= threshold, the