DOC add transform y section to faq.rst
@@ -385,3 +385,23 @@ efficient to process for most operations. Extensive work would also be needed
to support Pandas categorical types. Restricting input to homogeneous
types therefore reduces maintenance cost and encourages usage of efficient
data structures.
+
+Do you plan to implement transform for target y in a pipeline?
+----------------------------------------------------------------------------
+Currently transform only works for features X in a pipeline.
+There's a long-standing discussion about
+not being able to transform y in a pipeline.
+Follow on github issue
+`#4143<https://github.com/scikit-learn/scikit-learn/issues/4143>`_.
+Meanwhile check out
+:class:`sklearn.compose.TransformedTargetRegressor`,
+`pipegraph<https://github.com/mcasl/PipeGraph>`_,
+`imbalanced-learn<https://github.com/scikit-learn-contrib/imbalanced-learn>`_.
+Note that Scikit-learn solved for the case where y
+has an invertible transformation applied before training
+and inverted after prediction. Scikit-learn intends to solve for
+use cases where y should be transformed at training time
+and not at test time, for resampling and similar uses,
+like at imbalanced learn.
+In general, these use cases can be solved
+with a custom meta estimator rather than a Pipeline
