samronsin
diff --git a/‎maint_tools/test_docstrings.py‎
Lines changed: 0 additions & 1 deletion b/‎maint_tools/test_docstrings.py‎
Lines changed: 0 additions & 1 deletion
diff --git a/‎sklearn/decomposition/_sparse_pca.py‎
Lines changed: 9 additions & 6 deletions b/‎sklearn/decomposition/_sparse_pca.py‎
Lines changed: 9 additions & 6 deletions
@@ -142,7 +142,6 @@
     "SimpleImputer",
     "SkewedChi2Sampler",
     "SparseCoder",
-    "SparsePCA",
     "SparseRandomProjection",
     "SpectralBiclustering",
     "SpectralClustering",
 
@@ -40,6 +40,7 @@ class SparsePCA(TransformerMixin, BaseEstimator):
         Tolerance for the stopping condition.
 
     method : {'lars', 'cd'}, default='lars'
+        Method to be used for optimization.
         lars: uses the least angle regression method to solve the lasso problem
         (linear_model.lars_path)
         cd: uses the coordinate descent method to compute the
@@ -93,6 +94,13 @@ class SparsePCA(TransformerMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    See Also
+    --------
+    PCA : Principal Component Analysis implementation.
+    MiniBatchSparsePCA : Mini batch variant of `SparsePCA` that is faster but less
+        accurate.
+    DictionaryLearning : Generic dictionary learning problem using a sparse code.
+
     Examples
     --------
     >>> import numpy as np
@@ -108,12 +116,6 @@ class SparsePCA(TransformerMixin, BaseEstimator):
     >>> # most values in the components_ are zero (sparsity)
     >>> np.mean(transformer.components_ == 0)
     0.9666...
-
-    See Also
-    --------
-    PCA
-    MiniBatchSparsePCA
-    DictionaryLearning
     """
 
     def __init__(
@@ -153,6 +155,7 @@ def fit(self, X, y=None):
             and n_features is the number of features.
 
         y : Ignored
+            Not used, present here for API consistency by convention.
 
         Returns
         -------