DOC Ensures that NuSVR passes numpydoc validation (scikit-learn#20643)

jmloyola · web-flow · commit b4b2846719ac · 2021-08-02T10:32:59.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -72,7 +72,6 @@
     "NearestCentroid",
     "NeighborhoodComponentsAnalysis",
     "Normalizer",
-    "NuSVR",
     "Nystroem",
     "OPTICS",
     "OneVsOneClassifier",
diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py
@@ -408,6 +408,7 @@ def predict(self, X):
         Returns
         -------
         y_pred : ndarray of shape (n_samples,)
+            The predicted values.
         """
         X = self._validate_for_predict(X)
         predict = self._sparse_predict if self._sparse else self._dense_predict
diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py
@@ -1304,21 +1304,6 @@ class NuSVR(RegressorMixin, BaseLibSVM):
     support_vectors_ : ndarray of shape (n_SV, n_features)
         Support vectors.
 
-    Examples
-    --------
-    >>> from sklearn.svm import NuSVR
-    >>> from sklearn.pipeline import make_pipeline
-    >>> from sklearn.preprocessing import StandardScaler
-    >>> import numpy as np
-    >>> n_samples, n_features = 10, 5
-    >>> np.random.seed(0)
-    >>> y = np.random.randn(n_samples)
-    >>> X = np.random.randn(n_samples, n_features)
-    >>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1))
-    >>> regr.fit(X, y)
-    Pipeline(steps=[('standardscaler', StandardScaler()),
-                    ('nusvr', NuSVR(nu=0.1))])
-
     See Also
     --------
     NuSVC : Support Vector Machine for classification implemented with libsvm
@@ -1335,6 +1320,21 @@ class NuSVR(RegressorMixin, BaseLibSVM):
     .. [2] `Platt, John (1999). "Probabilistic outputs for support vector
         machines and comparison to regularizedlikelihood methods."
         <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
+
+    Examples
+    --------
+    >>> from sklearn.svm import NuSVR
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> import numpy as np
+    >>> n_samples, n_features = 10, 5
+    >>> np.random.seed(0)
+    >>> y = np.random.randn(n_samples)
+    >>> X = np.random.randn(n_samples, n_features)
+    >>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1))
+    >>> regr.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('nusvr', NuSVR(nu=0.1))])
     """
 
     _impl = "nu_svr"
