scikit-learn
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/linear_model/_omp.py‎
Lines changed: 23 additions & 21 deletions b/‎sklearn/linear_model/_omp.py‎
Lines changed: 23 additions & 21 deletions
@@ -17,7 +17,6 @@
     "MultiTaskElasticNet",
     "MultiTaskElasticNetCV",
     "MultiTaskLassoCV",
-    "OrthogonalMatchingPursuit",
     "OrthogonalMatchingPursuitCV",
     "PassiveAggressiveRegressor",
     "SparseRandomProjection",
 
@@ -604,7 +604,7 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):
         Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
 
     fit_intercept : bool, default=True
-        whether to calculate the intercept for this model. If set
+        Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
         (i.e. data is expected to be centered).
 
@@ -653,16 +653,18 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):
 
         .. versionadded:: 1.0
 
-    Examples
+    See Also
     --------
-    >>> from sklearn.linear_model import OrthogonalMatchingPursuit
-    >>> from sklearn.datasets import make_regression
-    >>> X, y = make_regression(noise=4, random_state=0)
-    >>> reg = OrthogonalMatchingPursuit(normalize=False).fit(X, y)
-    >>> reg.score(X, y)
-    0.9991...
-    >>> reg.predict(X[:1,])
-    array([-78.3854...])
+    orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
+    orthogonal_mp_gram :  Solves n_targets Orthogonal Matching Pursuit
+        problems using only the Gram matrix X.T * X and the product X.T * y.
+    lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
+    Lars : Least Angle Regression model a.k.a. LAR.
+    LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
+    sklearn.decomposition.sparse_encode : Generic sparse coding.
+        Each column of the result is the solution to a Lasso problem.
+    OrthogonalMatchingPursuitCV : Cross-validated
+        Orthogonal Matching Pursuit model (OMP).
 
     Notes
     -----
@@ -676,15 +678,16 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):
     Matching Pursuit Technical Report - CS Technion, April 2008.
     https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
 
-    See Also
+    Examples
     --------
-    orthogonal_mp
-    orthogonal_mp_gram
-    lars_path
-    Lars
-    LassoLars
-    sklearn.decomposition.sparse_encode
-    OrthogonalMatchingPursuitCV
+    >>> from sklearn.linear_model import OrthogonalMatchingPursuit
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(noise=4, random_state=0)
+    >>> reg = OrthogonalMatchingPursuit(normalize=False).fit(X, y)
+    >>> reg.score(X, y)
+    0.9991...
+    >>> reg.predict(X[:1,])
+    array([-78.3854...])
     """
 
     def __init__(
@@ -711,13 +714,12 @@ def fit(self, X, y):
             Training data.
 
         y : array-like of shape (n_samples,) or (n_samples, n_targets)
-            Target values. Will be cast to X's dtype if necessary
-
+            Target values. Will be cast to X's dtype if necessary.
 
         Returns
         -------
         self : object
-            returns an instance of self.
+            Returns an instance of self.
         """
         _normalize = _deprecate_normalize(
             self.normalize, default=True, estimator_name=self.__class__.__name__