scikit-learn
diff --git a/‎doc/whats_new/v1.1.rst
Lines changed: 9 additions & 0 deletions b/‎doc/whats_new/v1.1.rst
Lines changed: 9 additions & 0 deletions
diff --git a/‎sklearn/random_projection.py
Lines changed: 8 additions & 21 deletions b/‎sklearn/random_projection.py
Lines changed: 8 additions & 21 deletions
@@ -162,6 +162,15 @@ Changelog
   ndarray with `np.nan` when passed a `Float32` or `Float64` pandas extension
   array with `pd.NA`. :pr:`21278` by `Thomas Fan`_.
 
+:mod:`sklearn.random_projection`
+................................
+
+- |API| Adds :term:`get_feature_names_out` to all transformers in the
   :mod:`sklearn.random_projection` module:
+  :class:`~sklearn.random_projection.GaussianRandomProjection` and
+  :class:`~sklearn.random_projection.SparseRandomProjection`. :pr:`21330` by
+  :user:`Loïc Estève <lesteve>`.
+
 Code and Documentation Contributors
 -----------------------------------
 
 
@@ -34,7 +34,7 @@
 import scipy.sparse as sp
 
 from .base import BaseEstimator, TransformerMixin
-from .base import _check_feature_names_in
+from .base import _ClassNamePrefixFeaturesOutMixin
 
 from .utils import check_random_state
 from .utils.extmath import safe_sparse_dot
@@ -291,7 +291,9 @@ def _sparse_random_matrix(n_components, n_features, density="auto", random_state
         return np.sqrt(1 / density) / np.sqrt(n_components) * components
 
 
-class BaseRandomProjection(TransformerMixin, BaseEstimator, metaclass=ABCMeta):
+class BaseRandomProjection(
+    TransformerMixin, BaseEstimator, _ClassNamePrefixFeaturesOutMixin, metaclass=ABCMeta
+):
     """Base class for random projections.
 
     Warning: This class should not be used directly.
@@ -421,25 +423,10 @@ def transform(self, X):
         X_new = safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)
         return X_new
 
-    def get_feature_names_out(self, input_features=None):
-        """Get output feature names for transformation.
-
-        Parameters
-        ----------
-        input_features : array-like of str or None, default=None
-            Not used, present here for API consistency by convention.
-
-        Returns
-        -------
-        feature_names_out : ndarray of str objects
-            Transformed feature names.
-        """
-        _check_feature_names_in(self, input_features)
-        class_name = self.__class__.__name__.lower()
-        return np.asarray(
-            [f"{class_name}{i}" for i in range(self.n_components)],
-            dtype=object,
-        )
+    @property
+    def _n_features_out(self):
+        """Number of transformed output features."""
+        return self.n_components
 
 
 class GaussianRandomProjection(BaseRandomProjection):