diff --git a/scikits/learn/linear_model/logistic.py b/scikits/learn/linear_model/logistic.py
index a4a18b558491448bdc448e35b62c3999a19cce08..bd37a2fdaa0940c61f9c1f6ad7316cf70911a9ac 100644
--- a/scikits/learn/linear_model/logistic.py
+++ b/scikits/learn/linear_model/logistic.py
@@ -42,6 +42,13 @@ class LogisticRegression(BaseLibLinear, ClassifierMixin):
     --------
     LinearSVC
 
+    Notes
+    -----
+    The underlying C implementation uses a random number generator to
+    select features when fitting the model. It is thus not uncommon,
+    to have slightly different results for the same input data. If
+    that happens, try with a smaller eps parameter.
+
     References
     ----------
     LIBLINEAR -- A Library for Large Linear Classification
diff --git a/scikits/learn/linear_model/sparse/logistic.py b/scikits/learn/linear_model/sparse/logistic.py
index ca04d7ecd16e0bec3ecc1ba1a5efa043888edede..0cea75125aa4a18851dc5c1b40fe1f44e9468b70 100644
--- a/scikits/learn/linear_model/sparse/logistic.py
+++ b/scikits/learn/linear_model/sparse/logistic.py
@@ -45,18 +45,17 @@ class LogisticRegression(SparseBaseLibLinear, ClassifierMixin):
         intercept (a.k.a. bias) added to the decision function.
         It is available only when parameter intercept is set to True
 
-    Methods
-    -------
-    fit(X, Y) : self
-        Fit the model
-
-    predict(X) : array
-        Predict using the model.
-
     See also
     --------
     LinearSVC
 
+    Notes
+    -----
+    The underlying C implementation uses a random number generator to
+    select features when fitting the model. It is thus not uncommon,
+    to have slightly different results for the same input data. If
+    that happens, try with a smaller eps parameter.
+
     References
     ----------
     LIBLINEAR -- A Library for Large Linear Classification
diff --git a/scikits/learn/svm/liblinear.py b/scikits/learn/svm/liblinear.py
index e02be399d4f7ef7625ca358e16d6e1ba37c8c59d..7bc02de95d4a2c441c686b5d3059bbb5300f8df5 100644
--- a/scikits/learn/svm/liblinear.py
+++ b/scikits/learn/svm/liblinear.py
@@ -43,6 +43,17 @@ class LinearSVC(BaseLibLinear, ClassifierMixin):
     `intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
         Constants in decision function.
 
+    Notes
+    -----
+    The underlying C implementation uses a random number generator to
+    select features when fitting the model. It is thus not uncommon,
+    to have slightly different results for the same input data. If
+    that happens, try with a smaller eps parameter.
+
+    See also
+    --------
+    SVC
+
     References
     ----------
     LIBLINEAR -- A Library for Large Linear Classification
diff --git a/scikits/learn/svm/sparse/liblinear.py b/scikits/learn/svm/sparse/liblinear.py
index 57557b7f24d5f6fd04090b01fe1b4067aaf85ec7..acac283d77e5b50d331fc8f969c167b8d82bf283 100644
--- a/scikits/learn/svm/sparse/liblinear.py
+++ b/scikits/learn/svm/sparse/liblinear.py
@@ -32,7 +32,6 @@ class LinearSVC(SparseBaseLibLinear, ClassifierMixin):
         Select the algorithm to either solve the dual or primal
         optimization problem.
 
-
     Attributes
     ----------
     `coef_` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features]
@@ -42,6 +41,17 @@ class LinearSVC(SparseBaseLibLinear, ClassifierMixin):
     `intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
         constants in decision function
 
+    Notes
+    -----
+    The underlying C implementation uses a random number generator to
+    select features when fitting the model. It is thus not uncommon,
+    to have slightly different results for the same input data. If
+    that happens, try with a smaller eps parameter.
+
+    See also
+    --------
+    SVC
+
     References
     ----------
     LIBLINEAR -- A Library for Large Linear Classification