Added a comment on what independent_columns does during transformer f…

…itting

src/sensai/data_transformation/dft.py

@@ -715,6 +715,10 @@ 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: