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/_stochastic_gradient.py
Lines changed: 22 additions & 14 deletions b/‎sklearn/linear_model/_stochastic_gradient.py
Lines changed: 22 additions & 14 deletions
@@ -34,7 +34,6 @@
     "QuadraticDiscriminantAnalysis",
     "RandomizedSearchCV",
     "RobustScaler",
-    "SGDRegressor",
     "SelfTrainingClassifier",
     "SparseRandomProjection",
     "SpectralBiclustering",
 
@@ -1436,18 +1436,19 @@ def partial_fit(self, X, y, sample_weight=None):
         Parameters
         ----------
         X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Subset of training data
+            Subset of training data.
 
         y : numpy array of shape (n_samples,)
-            Subset of target values
+            Subset of target values.
 
         sample_weight : array-like, shape (n_samples,), default=None
             Weights applied to individual samples.
             If not provided, uniform weights are assumed.
 
         Returns
         -------
-        self : returns an instance of self.
+        self : object
+            Returns an instance of self.
         """
         self._validate_params(for_partial_fit=True)
         return self._partial_fit(
@@ -1521,10 +1522,10 @@ def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None):
         Parameters
         ----------
         X : {array-like, sparse matrix}, shape (n_samples, n_features)
-            Training data
+            Training data.
 
         y : ndarray of shape (n_samples,)
-            Target values
+            Target values.
 
         coef_init : ndarray of shape (n_features,), default=None
             The initial coefficients to warm-start the optimization.
@@ -1537,7 +1538,8 @@ def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None):
 
         Returns
         -------
-        self : returns an instance of self.
+        self : object
+            Fitted `SGDRegressor` estimator.
         """
         return self._fit(
             X,
@@ -1571,11 +1573,12 @@ def _decision_function(self, X):
         return scores.ravel()
 
     def predict(self, X):
-        """Predict using the linear model
+        """Predict using the linear model.
 
         Parameters
         ----------
         X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            Input data.
 
         Returns
         -------
@@ -1670,7 +1673,7 @@ def _fit_regressor(
 
 
 class SGDRegressor(BaseSGDRegressor):
-    """Linear model fitted by minimizing a regularized empirical loss with SGD
+    """Linear model fitted by minimizing a regularized empirical loss with SGD.
 
     SGD stands for Stochastic Gradient Descent: the gradient of the loss is
     estimated each sample at a time and the model is updated along the way with
@@ -1765,7 +1768,7 @@ class SGDRegressor(BaseSGDRegressor):
         Pass an int for reproducible output across multiple function calls.
         See :term:`Glossary <random_state>`.
 
-    learning_rate : string, default='invscaling'
+    learning_rate : str, default='invscaling'
         The learning rate schedule:
 
         - 'constant': `eta = eta0`
@@ -1861,6 +1864,16 @@ class SGDRegressor(BaseSGDRegressor):
 
         .. versionadded:: 1.0
 
+    See Also
+    --------
+    HuberRegressor : Linear regression model that is robust to outliers.
+    Lars : Least Angle Regression model.
+    Lasso : Linear Model trained with L1 prior as regularizer.
+    RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm.
+    Ridge : Linear least squares with l2 regularization.
+    sklearn.svm.SVR : Epsilon-Support Vector Regression.
+    TheilSenRegressor : Theil-Sen Estimator robust multivariate regression model.
+
     Examples
     --------
     >>> import numpy as np
@@ -1877,11 +1890,6 @@ class SGDRegressor(BaseSGDRegressor):
     >>> reg.fit(X, y)
     Pipeline(steps=[('standardscaler', StandardScaler()),
                     ('sgdregressor', SGDRegressor())])
-
-    See Also
-    --------
-    Ridge, ElasticNet, Lasso, sklearn.svm.SVR
-
     """
 
     def __init__(