diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py
index 6c15b81be98c2..ada175c7f32c6 100644
--- a/sklearn/compose/_column_transformer.py
+++ b/sklearn/compose/_column_transformer.py
@@ -141,6 +141,12 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
 
         .. versionadded:: 1.0
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying transformers expose such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     Notes
     -----
     The order of the columns in the transformed feature matrix follows the
diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py
index 12fe13ee848b9..af996623d8aa3 100644
--- a/sklearn/compose/_target.py
+++ b/sklearn/compose/_target.py
@@ -82,6 +82,12 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):
     transformer_ : object
         Transformer used in ``fit`` and ``predict``.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying regressor exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index 6fd57c9e8d4fc..848788647cf3f 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -261,7 +261,7 @@ def test_search_cv(estimator, check, request):
 #
 # check_classifiers_train would need to be updated with the error message
 N_FEATURES_IN_AFTER_FIT_MODULES_TO_IGNORE = {
-    'compose',
+    'feature_extraction',
     'model_selection',
     'multiclass',
     'multioutput',
diff --git a/sklearn/tests/test_metaestimators.py b/sklearn/tests/test_metaestimators.py
index ad716c3e4cd2f..c7412c98d4290 100644
--- a/sklearn/tests/test_metaestimators.py
+++ b/sklearn/tests/test_metaestimators.py
@@ -169,7 +169,7 @@ def _generate_meta_estimator_instances_with_pipeline():
     for _, Estimator in sorted(all_estimators()):
         sig = set(signature(Estimator).parameters)
 
-        if "estimator" in sig or "base_estimator" in sig:
+        if "estimator" in sig or "base_estimator" in sig or "regressor" in sig:
             if is_regressor(Estimator):
                 estimator = make_pipeline(TfidfVectorizer(), Ridge())
                 param_grid = {"ridge__alpha": [0.1, 1.0]}