DOC corrected wording in preprocessing.rst
@@ -34,8 +34,8 @@ standard deviation.
For instance, many elements used in the objective function of
a learning algorithm (such as the RBF kernel of Support Vector
-Machines or the l1 and l2 regularizers of linear models) assume that
-all features are centered around zero and have variance in the same
+Machines or the l1 and l2 regularizers of linear models) may assume that
+all features are centered around zero or have variance in the same
order. If a feature has a variance that is orders of magnitude larger
than others, it might dominate the objective function and make the
estimator unable to learn from other features correctly as expected.
