DOC Update doc for LinearSVC and supported loss with l1 penalty
@@ -453,7 +453,7 @@ Tips on Practical Use
set to ``False`` the underlying implementation of :class:`LinearSVC` is
not random and ``random_state`` has no effect on the results.
- * Using L1 penalization as provided by ``LinearSVC(loss='l2', penalty='l1',
+ * Using L1 penalization as provided by ``LinearSVC(penalty='l1',
dual=False)`` yields a sparse solution, i.e. only a subset of feature
weights is different from zero and contribute to the decision function.
Increasing ``C`` yields a more complex model (more features are selected).
