Added a comment on what independent_columns does during transformer f…

…itting

src/sensai/data_transformation/dft.py

@@ -707,6 +707,10 @@ def _tostring_additional_entries(self) -> Dict[str, Any]:
     def _fit(self, df: pd.DataFrame):
         matched_rules_by_column = {}
         self._rules = []
+        # For rules matching multiple columns, if independent_columns is False, the columns
+        # will be concatenated and treated as a single column for fitting the transformer.
+        # Note that transformers follow sklearn interfaces, thus just passing an array
+        # to them will learn a per-column-transformation. This will be the case for independent_columns=True.
         for rule in self._userRules:
             matching_columns = rule.matching_columns(df.columns)
             for c in matching_columns: