SGD and SVC duplicate example sorted

doc/modules/sgd.rst

@@ -159,8 +159,9 @@ further information.
 
  - :ref:`example_linear_model_plot_sgd_separating_hyperplane.py`,
  - :ref:`example_linear_model_plot_sgd_iris.py`
- - :ref:`example_linear_model_plot_sgd_weighted_classes.py`
  - :ref:`example_linear_model_plot_sgd_weighted_samples.py`
+ - :ref:`example_svm_plot_separating_hyperplane_unbalanced.py` (See the `Note`)
+
 
 Regression
 ==========

examples/svm/plot_separating_hyperplane_unbalanced.py

@@ -9,12 +9,27 @@ are unbalanced.
 We first find the separating plane with a plain SVC and then plot
 (dashed) the separating hyperplane with automatically correction for
 unbalanced classes.
+
+.. currentmodule:: sklearn.linear_model
+
+.. note::
+
+    This example will also work by replacing ``SVC(kernel="linear")``
+    with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
+    of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
+    such as that of a SVC with a linear kernel.
+
+    For example try instead of the ``SVC``::
+
+        clf = SGDClassifier(n_iter=100, alpha=0.01)
+
 """
 print(__doc__)
 
 import numpy as np
 import pylab as pl
 from sklearn import svm
+from sklearn.linear_model import SGDClassifier
 
 # we create 40 separable points
 rng = np.random.RandomState(0)