diff --git a/sklearn/svm/classes.py b/sklearn/svm/classes.py
index dbf5b78a1a6d1..0c98d9ffb5d3e 100644
--- a/sklearn/svm/classes.py
+++ b/sklearn/svm/classes.py
@@ -584,15 +584,22 @@ class SVC(BaseSVC):
 
     probA_ : array, shape = [n_class * (n_class-1) / 2]
     probB_ : array, shape = [n_class * (n_class-1) / 2]
-        If probability=True, the parameters learned in Platt scaling to
-        produce probability estimates from decision values. If
-        probability=False, an empty array. Platt scaling uses the logistic
-        function
+        If `probability=True`, it corresponds to the parameters learned in
+        Platt scaling to produce probability estimates from decision values.
+        If `probability=False`, it's an empty array. Platt scaling uses the
+        logistic function
         ``1 / (1 + exp(decision_value * probA_ + probB_))``
         where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
         more information on the multiclass case and training procedure see
         section 8 of [1]_.
 
+    class_weight_ : ndarray of shape (n_class,)
+        Multipliers of parameter C for each class.
+        Computed based on the ``class_weight`` parameter.
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
     Examples
     --------
     >>> import numpy as np
@@ -778,6 +785,27 @@ class NuSVC(BaseSVC):
     classes_ : array of shape = (n_classes,)
         The unique classes labels.
 
+    fit_status_ : int
+        0 if correctly fitted, 1 if the algorithm did not converge.
+
+    probA_ : ndarray, shape of (n_class * (n_class-1) / 2,)
+    probB_ : ndarray of shape (n_class * (n_class-1) / 2,)
+        If `probability=True`, it corresponds to the parameters learned in
+        Platt scaling to produce probability estimates from decision values.
+        If `probability=False`, it's an empty array. Platt scaling uses the
+        logistic function
+        ``1 / (1 + exp(decision_value * probA_ + probB_))``
+        where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
+        more information on the multiclass case and training procedure see
+        section 8 of [1]_.
+
+    class_weight_ : ndarray of shape (n_class,)
+        Multipliers of parameter C of each class.
+        Computed based on the ``class_weight`` parameter.
+
+    shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+        Array dimensions of training vector ``X``.
+
     Examples
     --------
     >>> import numpy as np