scikit-learn
diff --git a/‎doc/whats_new/v1.3.rst
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diff --git a/‎sklearn/utils/_set_output.py
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+6-1Lines changed: 6 additions & 1 deletion b/‎sklearn/utils/_set_output.py
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diff --git a/‎sklearn/utils/tests/test_set_output.py
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@@ -166,6 +166,9 @@ Changelog
 - |Feature| A `__sklearn_clone__` protocol is now available to override the
   default behavior of :func:`base.clone`. :pr:`24568` by `Thomas Fan`_.
 
+- |Fix| :class:`base.TransformerMixin` now currently keeps a namedtuple's class
+  if `transform` returns a namedtuple. :pr:`26121` by `Thomas Fan`_.
+
 :mod:`sklearn.calibration`
 ..........................
 
 
@@ -140,10 +140,15 @@ def wrapped(self, X, *args, **kwargs):
         data_to_wrap = f(self, X, *args, **kwargs)
         if isinstance(data_to_wrap, tuple):
             # only wrap the first output for cross decomposition
-            return (
+            return_tuple = (
                 _wrap_data_with_container(method, data_to_wrap[0], X, self),
                 *data_to_wrap[1:],
             )
+            # Support for namedtuples `_make` is a documented API for namedtuples:
+            # https://docs.python.org/3/library/collections.html#collections.somenamedtuple._make
+            if hasattr(type(data_to_wrap), "_make"):
+                return type(data_to_wrap)._make(return_tuple)
+            return return_tuple
 
         return _wrap_data_with_container(method, data_to_wrap, X, self)
 
 
@@ -1,4 +1,5 @@
 import pytest
+from collections import namedtuple
 
 import numpy as np
 from scipy.sparse import csr_matrix
@@ -292,3 +293,23 @@ def test_set_output_pandas_keep_index():
 
     X_trans = est.transform(X)
     assert_array_equal(X_trans.index, ["s0", "s1"])
+
+
+class EstimatorReturnTuple(_SetOutputMixin):
+    def __init__(self, OutputTuple):
+        self.OutputTuple = OutputTuple
+
+    def transform(self, X, y=None):
+        return self.OutputTuple(X, 2 * X)
+
+
+def test_set_output_named_tuple_out():
+    """Check that namedtuples are kept by default."""
+    Output = namedtuple("Output", "X, Y")
+    X = np.asarray([[1, 2, 3]])
+    est = EstimatorReturnTuple(OutputTuple=Output)
+    X_trans = est.transform(X)
+
+    assert isinstance(X_trans, Output)
+    assert_array_equal(X_trans.X, X)
+    assert_array_equal(X_trans.Y, 2 * X)